sentiment analysis research

Sentiment analysis, or opinion mining, is the process of analyzing large volumes of text to determine whether it expresses a positive sentiment, a negative sentiment or a neutral sentiment.

Companies now have access to more data about their customers than ever before, presenting both an opportunity and a challenge: analyzing the vast amounts of textual data available and extracting meaningful insights to guide their business decisions.

From emails and tweets to online survey responses, chats with customer service representatives and reviews, the sources available to gauge customer sentiment are seemingly endless. Sentiment analysis systems help companies better understand their customers, deliver stronger customer experiences and improve their brand reputation.

Discover the power of integrating a data lakehouse strategy into your data architecture, including enhancements to scale AI and cost optimization opportunities.

With more ways than ever for people to express their feelings online, organizations need powerful tools to monitor what’s being said about them and their products and services in near real time. As companies adopt sentiment analysis and begin using it to analyze more conversations and interactions, it will become easier to identify customer friction points at every stage of the customer journey.

Deliver more objective results from customer reviews

The latest artificial intelligence (AI) sentiment analysis tools help companies filter reviews and net promoter scores (NPS) for personal bias and get more objective opinions about their brand, products and services. For example, if a customer expresses a negative opinion along with a positive opinion in a review, a human assessing the review might label it negative before reaching the positive words. AI-enhanced sentiment classification helps sort and classify text in an objective manner, so this doesn’t happen, and both sentiments are reflected.  

Achieve greater scalability of business intelligence programs

Sentiment analysis enables companies with vast troves of unstructured data to analyze and extract meaningful insights from it quickly and efficiently. With the amount of text generated by customers across digital channels, it’s easy for human teams to get overwhelmed with information. Strong, cloud-based, AI-enhanced customer sentiment analysis tools help organizations deliver business intelligence from their customer data at scale, without expending unnecessary resources.

Perform real-time brand reputation monitoring

Modern enterprises need to respond quickly in a crisis. Opinions expressed on social media, whether true or not, can destroy a brand reputation that took years to build. Robust, AI-enhanced sentiment analysis tools help executives monitor the overall sentiment surrounding their brand so they can spot potential problems and address them swiftly.

Sentiment analysis uses natural language processing (NLP) and machine learning (ML) technologies to train computer software to analyze and interpret text in a way similar to humans. The software uses one of two approaches, rule-based or ML—or a combination of the two known as hybrid. Each approach has its strengths and weaknesses; while a rule-based approach can deliver results in near real-time, ML based approaches are more adaptable and can typically handle more complex scenarios.

Rule-based sentiment analysis

In the rule-based approach, software is trained to classify certain keywords in a block of text based on groups of words, or lexicons, that describe the author’s intent. For example, words in a positive lexicon might include “affordable,” “fast” and “well-made,” while words in a negative lexicon might feature “expensive,” “slow” and “poorly made”. The software then scans the classifier for the words in either the positive or negative lexicon and tallies up a total sentiment score based on the volume of words used and the sentiment score of each category.

Machine learning sentiment analysis

With a machine learning (ML) approach, an algorithm is used to train software to gauge sentiment in a block of text using words that appear in the text as well as the order in which they appear. Developers use sentiment analysis algorithms to teach software how to identify emotion in text similarly to the way humans do. ML models continue to “learn” from the data they are fed, hence the name “machine learning”. Here are a few of the most commonly used classification algorithms:

Linear regression: A statistics algorithm that describes a value (Y) based on a set of features (X).

Naive Bayes: An algorithm that uses Bayes’ theorem to categorize words in a block of text.

Support vector machines: A fast and efficient classification algorithm used to solve two-group classification problems.

Deep learning (DL): Also known as an artificial neural network, deep learning is an advanced machine learning technique that links together multiple algorithms to mimic human brain function.

The hybrid approach

A hybrid approach to text analysis combines both ML and rule-based capabilities to optimize accuracy and speed. While highly accurate, this approach requires more resources, such as time and technical capacity, than the other two.

In addition to the different approaches used to build sentiment analysis tools, there are also different types of sentiment analysis that organizations turn to depending on their needs. The three most popular types, emotion based, fine-grained and aspect-based sentiment analysis (ABSA) all rely on the underlying software’s capacity to gauge something called polarity, the overall feeling that is conveyed by a piece of text.

Generally speaking, a text’s polarity can be described as either positive, negative or neutral, but by categorizing the text even further, for example into subgroups such as “extremely positive” or “extremely negative,” some sentiment analysis models can identify more subtle and complex emotions. The polarity of a text is the most commonly used metric for gauging textual emotion and is expressed by the software as a numerical rating on a scale of one to 100. Zero represents a neutral sentiment and 100 represents the most extreme sentiment.

Here are the three most widely used types of sentiment analysis:

Fine-grained (graded)

Fine-grained, or graded, sentiment analysis is a type of sentiment analysis that groups text into different emotions and the level of emotion being expressed. The emotion is then graded on a scale of zero to 100, similar to the way consumer websites deploy star-ratings to measure customer satisfaction.

Aspect-based (ABSA)

Aspect based sentiment analysis (ABSA) narrows the scope of what’s being examined in a body of text to a singular aspect of a product, service or customer experience a business wishes to analyze. For example, a budget travel app might use ABSA to understand how intuitive a new user interface is or to gauge the effectiveness of a customer service chatbot. ABSA can help organizations better understand how their products are succeeding or falling short of customer expectations.

Emotional detection

Emotional detection sentiment analysis seeks to understand the psychological state of the individual behind a body of text, including their frame of mind when they were writing it and their intentions. It is more complex than either fine-grained or ABSA and is typically used to gain a deeper understanding of a person’s motivation or emotional state. Rather than using polarities, like positive, negative or neutral, emotional detection can identify specific emotions in a body of text such as frustration, indifference, restlessness and shock.

Organizations conduct sentiment analysis for a variety of reasons. Here are some of the most popular use cases.  

Support teams use sentiment analysis to deliver more personalized responses to customers that accurately reflect the mood of an interaction. AI-based chatbots that use sentiment analysis can spot problems that need to be escalated quickly and prioritize customers in need of urgent attention. ML algorithms deployed on customer support forums help rank topics by level-of-urgency and can even identify customer feedback that indicates frustration with a particular product or feature. These capabilities help customer support teams process requests faster and more efficiently and improve customer experience.

By using sentiment analysis to conduct social media monitoring brands can better understand what is being said about them online and why. For example, is a new product launch going well? Monitoring sales is one way to know, but will only show stakeholders part of the picture. Using sentiment analysis on customer review sites and social media to identify the emotions being expressed about the product will enable a far deeper understanding of how it is landing with customers.

By turning sentiment analysis tools on the market in general and not just on their own products, organizations can spot trends and identify new opportunities for growth. Maybe a competitor’s new campaign isn’t connecting with its audience the way they expected, or perhaps someone famous has used a product in a social media post increasing demand. Sentiment analysis tools can help spot trends in news articles, online reviews and on social media platforms, and alert decision makers in real time so they can take action.

While sentiment analysis and the technologies underpinning it are growing rapidly, it is still a relatively new field. According to “Sentiment Analysis,” by Liu Bing (2020) the term has only been widely used since 2003. 1 There is still much to be learned and refined, here are some of the most common drawbacks and challenges.

Lack of context

Context is a critical component for understanding what emotion is being expressed in a block of text and one that frequently causes sentiment analysis tools to make mistakes. On a customer survey, for example, a customer might give two answers to the question: “What did you like about our app?” The first answer might be “functionality” and the second, “UX”. If the question being asked was different, for example, “What didn’t you like about our app?” it changes the meaning of the customer’s response without changing the words themselves. To correct this problem, the algorithm would need to be given the original context of the question the customer was responding to, a time-consuming tactic known as pre or post  processing.

Use of irony and sarcasm

Regardless of the level or extent of its training, software has a hard time correctly identifying irony and sarcasm in a body of text. This is because often when someone is being sarcastic or ironic it’s conveyed through their tone of voice or facial expression and there is no discernable difference in the words they’re using. For example, when analyzing the phrase, “Awesome, another thousand-dollar parking ticket—just what I need,” a sentiment analysis tool would likely mistake the nature of the emotion being expressed and label it as positive because of the use of the word “awesome”.

Negation is when a negative word is used to convey a reversal of meaning in a sentence. For example, consider the sentence, “I wouldn’t say the shoes were cheap." What’s being expressed, is that the shoes were probably expensive, or at least moderately priced, but a sentiment analysis tool would likely miss this subtlety.  

Idiomatic language

Idiomatic language, such as the use of—for example—common English phrases like “Let’s not beat around the bush,” or “Break a leg ,” frequently confounds sentiment analysis tools and the ML algorithms that they’re built on. When human language phrases like the ones above are used on social media channels or in product reviews, sentiment analysis tools will either incorrectly identify them—the “break a leg” example could be incorrectly identified as something painful or sad, for example—or miss them completely.

Organizations who decide they want to deploy sentiment analysis to better understand their customers have two options for how they can go about it: either purchase an existing tool or build one of their own.

Businesses opting to build their own tool typically use an open-source library in a common coding language such as Python or Java. These libraries are useful because their communities are steeped in data science. Still, organizations looking to take this approach will need to make a considerable investment in hiring a team of engineers and data scientists.

Acquiring an existing software as a service (SaaS) sentiment analysis tool requires less initial investment and allows businesses to deploy a pre-trained machine learning model rather than create one from scratch. SaaS sentiment analysis tools can be up and running with just a few simple steps and are a good option for businesses who aren’t ready to make the investment necessary to build their own.

Today’s most effective customer support sentiment analysis solutions use the power of AI and ML to improve customer experiences. IBM watsonx Assistant is a market leading, conversational artificial intelligence platform powered by large language models (LLMs) that enables organizations to build AI-powered voice agents and chatbots that deliver superior automated self-service support to their customers on a simple, easy-to-use interface.

Discover how artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.

Gain a deeper understanding of machine learning along with important definitions, applications and concerns within businesses today.

Learn about the importance of mitigating bias in sentiment analysis and see how AI is being trained to be more neutral, unbiased and unwavering.

IBM watsonx Assistant helps organizations provide better customer experiences with an AI chatbot that understands the language of the business, connects to existing customer care systems, and deploys anywhere with enterprise security and scalability. watsonx Assistant automates repetitive tasks and uses machine learning to resolve customer support issues quickly and efficiently.

1 “Sentiment Analysis (Second edition),"  (link resides outside ibm.com), Liu, Bing, Cambridge University Press, September 23, 2020

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A survey of sentiment analysis: approaches, datasets, and future research.

1. Introduction

• A comprehensive overview of the state-of-the-art studies on sentiment analysis, which are categorized as conventional machine learning, deep learning, and ensemble learning, with a focus on the preprocessing techniques, feature extraction methods, classification methods, and datasets used, as well as the experimental results.
• An in-depth discussion of the commonly used sentiment analysis datasets and their challenges, as well as a discussion about the limitations of the current works and the potential for future research in this field.

2. Sentiment Analysis Algorithms

2.1. machine learning approach, 2.2. deep learning approach, 3. ensemble learning approach, 4. sentiment analysis datasets, 4.1. internet movie database (imdb), 4.2. twitter us airline sentiment, 4.3. sentiment140, 4.4. semeval-2017 task 4, 5. limitations and future research prospects.

• Poorly Structured and Sarcastic Texts: Many sentiment analysis methods rely on structured and grammatically correct text, which can lead to inaccuracies in analyzing informal and poorly structured texts, such as social media posts, slang, and sarcastic comments. This is because the sentiments expressed in these types of texts can be subtle and require contextual understanding beyond surface-level analysis.
• Coarse-Grained Sentiment Analysis: Although positive, negative, and neutral classes are commonly used in sentiment analysis, they may not capture the full range of emotions and intensities that a person can express. Fine-grained sentiment analysis, which categorizes emotions into more specific categories such as happy, sad, angry, or surprised, can provide more nuanced insights into the sentiment expressed in a text.
• Lack of Cultural Awareness: Sentiment analysis models trained on data from a specific language or culture may not accurately capture the sentiments expressed in texts from other languages or cultures. This is because the use of language, idioms, and expressions can vary widely across cultures, and a sentiment analysis model trained on one culture may not be effective in analyzing sentiment in another culture.
• Dependence on Annotated Data: Sentiment analysis algorithms often rely on annotated data, where humans manually label the sentiment of a text. However, collecting and labeling a large dataset can be time-consuming and resource-intensive, which can limit the scope of analysis to a specific domain or language.
• Shortcomings of Word Embeddings: Word embeddings, which are a popular technique used in deep learning-based sentiment analysis, can be limited in capturing the complex relationships between words and their meanings in a text. This can result in a model that does not accurately represent the sentiment expressed in a text, leading to inaccuracies in analysis.
• Bias in Training Data: The training data used to train a sentiment analysis model can be biased, which can impact the model’s accuracy and generalization to new data. For example, a dataset that is predominantly composed of texts from one gender or race can lead to a model that is biased toward that group, resulting in inaccurate predictions for texts from other groups.
• Fine-Grained Sentiment Analysis: The current sentiment analysis models mainly classify the sentiment into three coarse classes: positive, negative, and neutral. However, there is a need to extend this to a fine-grained sentiment analysis, which consists of different emotional intensities, such as strongly positive, positive, neutral, negative, and strongly negative. Researchers can explore various deep learning architectures and techniques to perform fine-grained sentiment analysis. One such approach is to use hierarchical attention networks that can capture the sentiment expressed in different parts of a text at different levels of granularity.
• Sentiment Quantification: Sentiment quantification is an important application of sentiment analysis. It involves computing the polarity distributions based on the topics to aid in strategic decision making. Researchers can develop more advanced models that can accurately capture the sentiment distribution across different topics. One way to achieve this is to use topic modeling techniques to identify the underlying topics in a corpus of text and then use sentiment analysis to compute the sentiment distribution for each topic.
• Handling Ambiguous and Sarcastic Texts: Sentiment analysis models face challenges in accurately detecting sentiment in ambiguous and sarcastic texts. Researchers can explore the use of reinforcement learning techniques to train models that can handle ambiguous and sarcastic texts. This involves developing models that can learn from feedback and adapt their predictions accordingly.
• Cross-lingual Sentiment Analysis: Currently, sentiment analysis models are primarily trained on English text. However, there is a growing need for sentiment analysis models that can work across multiple languages. Cross-lingual sentiment analysis would help to better understand the sentiment expressed in different languages, making sentiment analysis accessible to a larger audience. Researchers can explore the use of transfer learning techniques to develop sentiment analysis models that can work across multiple languages. One approach is to pretrain models on large multilingual corpora and then fine-tune them for sentiment analysis tasks in specific languages.
• Sentiment Analysis in Social Media: Social media platforms generate huge amounts of data every day, making it difficult to manually process the data. Researchers can explore the use of domain-specific embeddings that are trained on social media text to improve the accuracy of sentiment analysis models. They can also develop models that can handle noisy or short social media text by incorporating contextual information and leveraging user interactions.

6. Conclusions

Author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Click here to enlarge figure

Literature	Features	Classifier	Dataset	Accuracy (%)
Jung et al. (2016) [ ]		MNB	Sentiment140	85
Athindran et al. (2018) [ ]		NB	Self-collected dataset (from Tweets)	77
Vanaja et al. (2018) [ ]	A priori algorithm	NB, SVM	Self-collected dataset (from Amazon)	83.42
Iqbal et al. (2018) [ ]	Unigram, Bigram	NB, SVM, ME	IMDb	88
			Sentiment140	90
Rathi et al. (2018) [ ]	TF-IDF	DT	Sentiment140, Polarity Dataset, and University of Michigan dataset	84
		AdaBoost		67
		SVM		82
Hemakala and Santhoshkumar (2018) [ ]		AdaBoost	Indian Airlines	84.5
Tariyal et al. (2018) [ ]		Regression Tree	Own dataset	88.99
Rahat et al. (2019) [ ]		SVC	Airline review	82.48
		MNB		76.56
Makhmudah et al. (2019) [ ]	TF-IDF	SVM	Tweets related to homosexuals	99.5
Wongkar and Angdresey (2019)  [ ]		NB	Twitter (2019 presidential candidates of the Republic of Indonesia)	75.58
Madhuri (2019) [ ]		SVM	Twitter (Indian Railways)	91.5
Gupta et al. (2019) [ ]	TF-IDF	Neural Network	Sentiment140	80
Prabhakar et al. (2019) [ ]		AdaBoost (Bagging and Boosting)	Skytrax and Twitter (Airlines)	68 F-score
Hourrane et al. (2019) [ ]	TF-IDF	Ridge Classifier	IMDb	90.54
			Sentiment 140	76.84
Alsalman (2020) [ ]	TF-IDF	MNB	Arabic Tweets	87.5
Saad et al. (2020) [ ]	Bag of Words	SVM	Twitter US Airline Sentiment	83.31
Alzyout et al. (2021) [ ]	TF-IDF	SVM	Self-collected dataset	78.25
Jemai et al. (2021) [ ]		NB	NLTK corpus	99.73

Literature	Embedding	Classifier	Dataset	Accuracy (%)
Ramadhani et al. (2017) [ ]		MLP	Korean and English Tweets	75.03
Demirci et al. (2019) [ ]	word2vec	MLP	Turkish Tweets	81.86
Raza et al. (2021) [ ]	Count Vectorizer and TF-IDF Vectorizer	MLP	COVID-19 reviews	93.73
Dholpuria et al. (2018) [ ]		CNN	IMDb (3000 reviews)	99.33
Harjule et al. (2020) [ ]		LSTM	Twitter US Airline Sentiment	82
			Sentiment140	66
Uddin et al. (2019) [ ]		LSTM	Bangla Tweets	86.3
Alahmary and Al-Dossari (2018) [ ]	word2vec	BiLSTM	Saudi dialect Tweets	94
Yang (2018) [ ]	GloVe	Recurrent neural filter-based CNN and LSTM	Stanford Sentiment Treebank	53.4
Goularas and Kamis (2019) [ ]	word2vec and GloVe	CNN and LSTM	Tweets from semantic evaluation	59
Hossain and Bhuiyan (2019)  [ ]	word2vec	CNN and LSTM	Foodpanda and Shohoz Food	75.01
Tyagi et al. (2020) [ ]	GloVe	CNN and BiLSTM	Sentiment140	81.20
Rhanoui et al. (2019) [ ]	doc2vec	CNN and BiLSTM	French articles and international news	90.66
Jang et al. (2020) [ ]	word2vec	hybrid CNN and BiLSTM	IMDb	90.26
Chundi et al. (2020) [ ]		Convolutional BiLSTM	English, Kannada, and a mixture of both languages	77.6
Thinh et al. (2019) [ ]		1D-CNN with GRU	IMDb	90.02
Janardhana et al. (2020) [ ]	GloVe	Convolutional RNN	Movie reviews	84
Chowdhury et al. (2020) [ ]	word2vec, GloVe, and sentiment-specific word embedding	BiLSTM	Twitter US Airline Sentiment	81.20
Vimali and Murugan (2021) [ ]		BiLSTM	Self-collected	90.26
Anbukkarasi and Varadhaganapathy (2020) [ ]		DBLSTM	Self-collected (Tamil Tweets)	86.2
Kumar and Chinnalagu (2020) [ ]		SAB-LSTM	Self-collected	29 (POS) 50 (NEG) 21 (NEU)
Hossen et al. (2021) [ ]		LSTM	Self-collected	86
		GRU		84
Younas et al. (2020) [ ]		mBERT	Pakistan elections in 2018 (Tweets)	69
		XLM-R		71
Dhola and Saradva (2021) [ ]		BERT	Sentiment140	85.4
Tan et a. (2022) [ ]		RoBERTa-LSTM	IMDb	92.96
			Twitter US Airline Sentiment	91.37
			Sentiment140	89.70
Kokab et al. (2022) [ ]	BERT	CBRNN	US airline reviews	97
			Self-driving car reviews	90
			US presidential election reviews	96
			IMDb	93
AlBadani et al. (2022) [ ]	ST-GCN	ST-GCN	SST-B	95.43
			IMDB	94.94
			Yelp 2014	72.7
Tiwari and Nagpal (2022) [ ]	BERT	KEAHT	COVID-19 vaccine	91
			Indian Farmer Protests	81.49
Tesfagergish et al. (2022) [ ]	Zero-shot transformer	Ensemble learning	SemEval 2017	87.3
Maghsoudi et al. (2022) [ ]	Transformer	DST	Self-collected	84
Jing and Yang (2022) [ ]	Light-Transformer	Light-Transformer	NLPCC2014 Task2	76.40

Literature	Feature Extractor	Classifier	Dataset	Accuracy (%)
Alrehili et al. (2019) [ ]		NB + SVM + RF + Bagging + Boosting	Self-collected	89.4
Bian et al. (2019) [ ]	TF-IDF	LR + SVM + KNN	COVID-19 reviews	98.99
Gifari and Lhaksmana (2021) [ ]	TF-IDF	MNB + KNN + LR	IMDb	89.40
Parveen et al. (2020) [ ]		MNB + BNB + LR + LSVM + NSVM	Movie reviews	91
Aziz and Dimililer (2020) [ ]	TF-IDF	NB + LR + SGD + RF + DT + SVM	SemEval-2017 4A	72.95
			SemEval-2017 4B	90.8
			SemEval-2017 4C	68.89
Varshney et al. (2020) [ ]	TF-IDF	LR + NB + SGD	Sentiment140	80
Athar et al. (2021) [ ]	TF-IDF	LR + NB + XGBoost + RF + MLP	IMDb	89.9
Nguyen and Nguyen (2018) [ ]	TF-IDF, word2vec	LR + SVM + CNN + LSTM (Mean)	Vietnamese Sentiment	69.71
		LR + SVM + CNN + LSTM (Vote)	Vietnamese Sentiment Food Reviews	89.19
		LR + SVM + CNN + LSTM (Vote)	Vietnamese Sentiment	92.80
Kamruzzaman et al.(2021) [ ]	GloVe	7-Layer CNN + GRU + GloVe	Grammar and Online Product Reviews	94.19
	Attention embedding	7-Layer CNN + LSTM + Attention Layer	Restaurant Reviews	96.37
Al Wazrah and Alhumoud (2021) [ ]	AraVec	SGRU + SBi-GRU + AraBERT	Arabic Sentiment Analysis	90.21
Tan et a. (2022) [ ]		RoBERTa-LSTM + RoBERTa-BiLSTM + RoBERTa-GRU	IMDb	94.9
			Twitter US Airline Sentiment	91.77
			Sentiment140	89.81

Dataset	Classes	Strongly Positive	Positive	Neutral	Negative	Strongly Negative	Total
IMDb	2	-	25,000	-	25,000	-	50,000
Twitter US Airline Sentiment	3	-	2363	3099	9178	-	14,160
Sentiment140	2	-	800,000	-	800,000	-	1,600,000
SemEval-2017 4A	3	-	22,277	28,528	11,812	-	62,617
SemEval-2017 4B	2	-	17,414	-	7735	-	25,149
SemEval-2017 4C	5	1151	15,254	19,187	6943	476	43,011
SemEval-2017 4D	2	-	17,414	-	7735	-	25,149
SemEval-2017 4E	5	1151	15,254	19,187	6943	476	43,011

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Tan, K.L.; Lee, C.P.; Lim, K.M. A Survey of Sentiment Analysis: Approaches, Datasets, and Future Research. Appl. Sci. 2023 , 13 , 4550. https://doi.org/10.3390/app13074550

Tan KL, Lee CP, Lim KM. A Survey of Sentiment Analysis: Approaches, Datasets, and Future Research. Applied Sciences . 2023; 13(7):4550. https://doi.org/10.3390/app13074550

Tan, Kian Long, Chin Poo Lee, and Kian Ming Lim. 2023. "A Survey of Sentiment Analysis: Approaches, Datasets, and Future Research" Applied Sciences 13, no. 7: 4550. https://doi.org/10.3390/app13074550

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Sentiment Analysis: Decoding Emotions for Research

Introduction

What is sentiment analysis, what is an example of sentiment analysis, why is sentiment analysis important, how do you collect sentiments, how do you analyze sentiments, what are the current challenges for sentiment analysis.

Sentiment analysis is the process of determining whether textual data contains a positive sentiment or a negative sentiment. Researchers use sentiment analysis tools to provide additional clarity and context to the messages conveyed in words to deliver more meaningful insights.

In this article, we'll look at the importance of sentiments, how researchers analyze sentiments, and what strategies and tools can help you in your research .

Sentiment analysis is a subset of natural language processing (NLP) that focuses on extracting and understanding the emotional content from data . The primary objective is to classify the polarity of a text as positive, negative, or neutral. This classification is essential for understanding customer sentiment, gauging public opinion, and conducting in-depth research on various topics.

At its core, a sentiment analysis system employs machine learning techniques and algorithms to dissect the language used in text data from many sources, such as:

• written feedback
• news articles
• survey records
• social media posts

One of the most refined forms of this method is aspect-based sentiment analysis. Rather than merely classifying the overall sentiment of a document, this kind of analysis pinpoints specific topics or aspects within the text and evaluates the sentiment towards each. Such sentiment analysis technologies with natural language processing can also be used for opinion mining.

A simple example

Consider a product review that states, "The camera on this phone is excellent, but the battery life is short." A sentiment analysis model would recognize the positive sentiment towards the camera and the negative sentiment towards the battery life, rather than giving a blanket sentiment score.

Sentiment analysis tools are varied, ranging from simple models that identify positive and negative terms to sophisticated sentiment analysis models that rely on machine learning and data scientists for insightful sentiment analysis. Such tools work by assigning a sentiment score to words or phrases, often based on their context. The result? A sentiment analysis solution that deciphers the nuances of human language, turning unstructured data into actionable insights.

Ultimately, an accurate sentiment analysis bridges the gap between the vast world of text-based data and the need to understand the underlying emotions and opinions it contains. Whether you're a researcher looking to perform sentiment analysis on news articles or a business keen on understanding customer feedback, sentiment analysis is a pivotal tool in today's data-driven world.

For deeper insights, turn to ATLAS.ti

Make the most of your data with the most comprehensive qualitative data analysis available. Download a free trial today.

Sentiment analysis offers tangible examples of its applications across diverse fields. From businesses striving to enhance their products to researchers aiming to grasp public sentiment on various issues, the power of sentiment analysis is evident.

By examining specific sectors, we can better understand the profound impact this analysis has on our decision-making processes and the vast potential it holds in shaping perceptions.

Market research

Conducting market research often consists of analyzing sentiment to gauge public reactions to a product or service. Using sentiment analysis tools, companies can sift through survey responses and online reviews, identifying patterns that might not be immediately apparent.

For example, if a new beverage receives predominantly positive reviews for its taste but negative comments about its packaging, this analytical approach can highlight these specific sentiments, guiding the company in refining its offering.

Customer feedback

Customer feedback is a goldmine of sentiment analysis datasets for businesses aiming to improve their services. By implementing a sentiment analysis system, companies can categorize feedback as positive, negative, or neutral, making it easier to prioritize areas for improvement.

Suppose a hotel chain discovers that a significant number of negative words in customer reviews pertain to room cleanliness. In that case, they can take immediate measures to address this concern, enhancing the overall guest experience.

Social media platforms

Social media is awash with opinions and feedback. By employing models for the analysis of sentiments, businesses and researchers can tap into real-time feelings of the masses.

For instance, if a celebrity endorses a brand and sentiment analysis reflects a surge in positive words associated with that brand, it can be concluded that the endorsement had a favorable impact. Conversely, if a political figure makes a statement and the analysis indicates a spike in negative words related to the topic, it provides insights into public opinion.

Sentiment analysis has rapidly become a crucial tool in today's digital age, helping businesses, researchers, and individuals decode the emotions hidden within vast amounts of data. But why has it garnered such significance?

The reasons are manifold, but they all converge on the idea that understanding sentiment offers a deeper, more nuanced view of human reactions and opinions.

Sentiment analysis use cases & applications

The applications of sentiment analysis are diverse and expansive. For instance, in the realm of politics, sentiment analysis can be used to gauge public opinion on policies or candidates, offering insights that can guide campaign strategies.

In the healthcare sector, sentiment analysis can capture patient feedback, allowing providers to fine-tune their services and improve patient experiences.

Moreover, educators can use sentiment analysis to understand student feedback, making curriculum adjustments that align with student needs and preferences.

Benefits of sentiment analysis

Beyond its various applications, the benefits of sentiment analysis are profound. Firstly, it offers an efficient way to process large volumes of unstructured data , turning it into actionable insights. Businesses, for example, can use sentiment analysis to get ahead of potential public relations crises by identifying negative sentiments early.

Furthermore, it provides rule-based systems that can circumvent the time-consuming task of manually reviewing each piece of feedback. This not only saves time but also reduces the risk of human bias.

Most significantly, by understanding both positive and negative phrases and their context, organizations can better align their strategies and offerings with their audience's true feelings and needs.

Collecting sentiments involves gathering data from various sources to be analyzed for emotional content. This task, while seemingly straightforward, requires a systematic approach to ensure that the data obtained is both relevant and of high quality.

One of the primary sources for sentiment collection is social media platforms. Platforms like Twitter, Facebook, and Instagram are brimming with user-generated content that reflects public opinion on a vast array of topics. By utilizing specialized web scraping tools or APIs provided by these platforms, one can amass large datasets of posts, comments, and reviews to analyze.

Customer reviews on e-commerce websites, such as Amazon or Yelp, are another treasure trove of sentiments. These reviews often provide detailed insights into customer sentiment about products, services, and overall brand perception. Similarly, survey responses, when designed with open-ended questions, can provide valuable data that captures the sentiments of the respondents.

In the news and media sector, news articles and op-eds are rich sources of sentiment. Collecting sentiments from these sources can help gauge public sentiment on current events, governmental decisions, or societal issues.

Forums and online communities, like Reddit or specialized industry forums, offer another avenue. Here, users often engage in in-depth discussions, providing nuanced views that are ripe for sentiment analysis.

However, while collecting sentiments, it's essential to consider privacy and ethical guidelines. Ensuring that data is anonymized and devoid of personally identifiable information is crucial. Moreover, always be aware of terms of service when extracting data from online platforms, as some might have restrictions on data scraping.

Analyzing sentiments is a multifaceted process that goes beyond merely identifying positive or negative words. It examines the context, nuances, and the intricate elements of human language. With advancements in machine learning and data science, this analysis has become more refined and precise.

Sentiment scores

At the foundation of this analytical approach lies the sentiment score. This score is usually a numerical value assigned to a piece of text, indicating its overall sentiment. For instance, a system to analyze sentiment might assign values on a scale from -1 (negative) to 1 (positive), with 0 representing a neutral sentiment. Sentiment scores provide a quick overview, enabling researchers and businesses to categorize large datasets swiftly.

Sentiment analysis algorithms

A machine learning algorithm, natural language toolkit, or artificial neural networks can power sentiment analysis work. These range from simple rule-based algorithms, which identify sentiments based on predefined lists of positive and negative words, to more complex machine learning techniques. Machine learning-based sentiment analysis models, especially those utilizing deep learning, consider the broader context in which words are used, leading to more advanced sentiment analysis.

Sentiment analysis tools

There's a plethora of tools available, each tailored for different requirements. Some tools are designed for specific industries, while others are more general-purpose. Many of these tools leverage advanced models, making it easier for users without a deep technical background to extract meaningful insights from textual data. The qualitative data analysis software ATLAS.ti, for example, includes a sentiment analysis tool to automatically code data .

Sentiment analysis, despite its transformative potential and growing adoption, is not without its share of challenges. The intricacies of language and emotion often pose complexities that even the most advanced systems can find challenging to navigate.

Sarcasm and irony : One of the most significant challenges is detecting sarcasm and irony. A statement like "Oh, great! Another flat tire!" may be classified as positive by rudimentary analysis models because of the word "great." However, the context clearly indicates a negative sentiment.

Cultural nuances : Cultural and regional variations in language can affect sentiment interpretation. A word or phrase that's considered positive in one culture might be neutral or even negative in another. Without a culturally-aware model, these nuances can easily be missed.

Short and ambiguous texts : Platforms like Twitter, with their character limitations, often contain short and sometimes ambiguous messages. Without ample context, determining the sentiment of such messages can be tricky.

Polysemy : Words with multiple meanings, based on context, can pose challenges. For instance, the word "light" can be positive when referring to a "light meal" but negative when talking about "light rain" during a planned outdoor event.

Emotionally complex statements : Some statements might contain mixed emotions, making them hard to classify. For example, "I love how this camera captures colors, but its weight is a bit much for me." This statement contains both positive and negative sentiments about the same product.

Evolution of language : Language is dynamic. New words, slang, and expressions constantly emerge, especially on digital platforms. Keeping sentiment analysis tools updated to recognize and correctly interpret these new terms is a continual challenge.

Addressing these challenges requires a combination of improved algorithms, larger and more diverse training datasets, and a deeper understanding of linguistics and cultural contexts. As technology advances and sentiment analysis solutions become more sophisticated, the hope is that these challenges will diminish, leading to even more accurate and insightful outcomes.

Make ATLAS.ti your own sentiment analysis solution

Powerful auto-coding tools for sentiment analysis and opinion mining are at your fingertips, starting with a free trial.

A Complete Guide to Sentiment Analysis

“That movie was a colossal disaster… I absolutely hated it! Waste of time and money #skipit”

“Have you seen the new season of XYZ? It is so good!”

“You should really check out this new app, it’s awesome! And it makes your life so convenient.”

By reading these comments, can you figure out what the emotions behind them are?

They may seem obvious to you because we, as humans, are capable of discerning the complex emotional sentiments behind the text.

Not only have we been educated to understand the meanings, intentions, and grammar behind each of these particular sentences, but we’ve also personally felt many of these emotions before and, from our own experiences, can conjure up the deeper meaning behind these words.

Moreover, we’re also extremely familiar with the real-world objects that the text is referring to.

This doesn’t apply to machines, but they do have other ways of determining positive and negative sentiments! How do they do this, exactly? By using sentiment analysis. In this article, we will discuss how a computer can decipher emotions by using sentiment analysis methods, and what the implications of this can be. If you want to skip ahead to a certain section, simply use the clickable menu:

• What is sentiment analysis?
• How does sentiment analysis work?
• Sentiment analysis use cases
• Machine learning and sentiment analysis
• Advantages of sentiment analysis
• Disadvantages of sentiment analysis
• Key takeaways and next steps

1. What is sentiment analysis?

With computers getting smarter and smarter, surely they’re able to decipher and discern between the wide range of different human emotions, right?

Wrong—while they are intelligent machines, computers can neither see nor feel any emotions, with the only input they receive being in the form of zeros and ones—or what’s more commonly known as binary code.

However, on the other hand, computers excel at the one thing that humans struggle with: processing large amounts of data quickly and effectively. So, theoretically, if we could teach machines how to identify the sentiments behind the plain text, we could analyze and evaluate the emotional response to a certain product by analyzing hundreds of thousands of reviews or tweets.

This would, in turn, provide companies with invaluable feedback and help them tailor their next product to better suit the market’s needs. So, what kind of process is this? Sentiment analysis!

Sentiment analysis, also known as opinion mining , is the process of determining the emotions behind a piece of text. Sentiment analysis aims to categorize the given text as positive, negative, or neutral.

Furthermore, it then identifies and quantifies subjective information about those texts with the help of:

• natural language processing (NLP)
• text analysis
• computational linguistics
• machine learning

2. How does sentiment analysis work?

There are two main methods for sentiment analysis: machine learning and lexicon-based.

The machine learning method leverages human-labeled data to train the text classifier, making it a supervised learning method.

The lexicon-based approach breaks down a sentence into words and scores each word’s semantic orientation based on a dictionary. It then adds up the various scores to arrive at a conclusion.

In this example, we will look at how sentiment analysis works using a simple lexicon-based approach. We’ll take the following comment as our test data:

Step 1: Cleaning

The initial step is to remove special characters and numbers from the text. In our example, we’ll remove the exclamation marks and commas from the comment above.

That movie was a colossal disaster I absolutely hated it Waste of time and money skipit

Step 2: Tokenization

Tokenization is the process of breaking down a text into smaller chunks called tokens, which are either individual words or short sentences.

Breaking down a paragraph into sentences is known as sentence tokenization , and breaking down a sentence into words is known as word tokenization .

[ ‘That’, ‘movie’, ‘was’, ‘a’, ‘colossal’, ‘disaster’, ‘I’, ‘absolutely’, ‘hated’, ‘it’,  ‘Waste’, ‘of’, ‘time’, ‘and’, ‘money’, ‘skipit’ ]

Step 3: Part-of-speech (POS) tagging

Part-of-speech tagging is the process of tagging each word with its grammatical group, categorizing it as either a noun, pronoun, adjective, or adverb—depending on its context.

This transforms each token into a tuple of the form (word, tag). POS tagging is used to preserve the context of a word.

[ (‘That’, ‘DT’), 

  (‘movie’, ‘NN’), 

  (‘was’, ‘VBD’),  

  (‘a’, ‘DT’) 

  (‘colossal’, ‘JJ’), 

  (‘disaster’, ‘NN’),  

  (‘I’, ‘PRP’), 

  (‘absolutely’, ‘RB’), 

  (‘hated’, ‘VBD’), 

  (‘it’, ‘PRP’),  

  (‘Waste’, ‘NN’) , 

  (‘of’, ‘IN’), 

  (‘time’, ‘NN’), 

  (‘and’, ‘CC’),

  (‘money’, ‘NN’),  

  (‘skipit’, ‘NN’) ]

Step 4: Removing stop words

Stop words are words like ‘have,’ ‘but,’ ‘we,’ ‘he,’ ‘into,’ ‘just,’ and so on. These words carry information of little value, andare generally considered noise, so they are removed from the data.

[ ‘movie’, ‘colossal’, ‘disaster’, ‘absolutely’, ‘hated’, Waste’, ‘time’, ‘money’, ‘skipit’ ]

Step 5: Stemming

Stemming is a process of linguistic normalization which removes the suffix of each of these words and reduces them to their base word. For example, loved is reduced to love, wasted is reduced to waste. Here, hated is reduced to hate.

[ ‘movie’, ‘colossal’, ‘disaster’, ‘absolutely’, ‘hate’, ‘Waste’, ‘time’, ‘money’, ‘skipit’ ]

Step 6: Final Analysis

In a lexicon-based approach, the remaining words are compared against the sentiment libraries, and the scores obtained for each token are added or averaged.

Sentiment libraries are a list of predefined words and phrases which are manually scored by humans. For example, ‘worst’ is scored -3, and ‘amazing’ is scored +3. 

With a basic dictionary, our example comment will be turned into:

movie= 0, colossal= 0, disaster= -2,  absolutely=0, hate=-2, waste= -1, time= 0, money= 0, skipit= 0

This makes the overall score of the comment -5 , classifying the comment as negative.

3. Sentiment analysis use cases

Sentiment analysis is used to swiftly glean insights from enormous amounts of text data, with its applications ranging from politics, finance, retail, hospitality, and healthcare. For instance, consider its usefulness in the following scenarios:

• Brand reputation management:  Sentiment analysis allows you to track all the online chatter about your brand and spot potential PR disasters before they become major concerns. 
• Voice of the customer: The “voice of the customer” refers to the feedback and opinions you get from your clients all over the world. You can improve your product and meet your clients’ needs with the help of this feedback and sentiment analysis.
• Voice of the employee:   Employee satisfaction can be measured for your company by analyzing reviews on sites like Glassdoor, allowing you to determine how to improve the work environment you have created.
• Market research: You can analyze and monitor internet reviews of your products and those of your competitors to see how the public differentiates between them, helping you glean indispensable feedback and refine your products and marketing strategies accordingly. Furthermore, sentiment analysis in market research can also anticipate future trends and thus have a first-mover advantage.

Other applications for sentiment analysis could include:

• Customer support
• Social media monitoring
• Voice assistants & chatbots
• Election polls
• Customer experience about a product
• Stock market sentiment and market movement
• Analyzing movie reviews

4. Machine learning and sentiment analysis

Sentiment analysis tasks are typically treated as classification problems in the machine learning approach.

Data analysts use historical textual data—which is manually labeled as positive, negative, or neutral—as the training set. They then complete feature extraction on this labeled dataset, using this initial data to train the model to recognize the relevant patterns. Next, they can accurately predict the sentiment of a fresh piece of text using our trained model.

Naive Bayes, logistic regression, support vector machines, and neural networks are some of the classification algorithms commonly used in sentiment analysis tasks. The high accuracy of prediction is one of the key advantages of the machine learning approach.

5. Advantages of sentiment analysis

Considering large amounts of data on the internet are entirely unstructured, data analysts need a way to evaluate this data.

With regards to sentiment analysis, data analysts want to extract and identify emotions, attitudes, and opinions from our sample sets. Reading and assigning a rating to a large number of reviews, tweets, and comments is not an easy task, but with the help of sentiment analysis, this can be accomplished quickly.

Another unparalleled feature of sentiment analysis is its ability to quickly analyze data such as new product launches or new policy proposals in real time. Thus, sentiment analysis can be a cost-effective and efficient way to gauge and accordingly manage public opinion.

6. Disadvantages of sentiment analysis

Sentiment analysis, as fascinating as it is, is not without its flaws.

Human language is nuanced and often far from straightforward. Machines might struggle to identify the emotions behind an individual piece of text despite their extensive grasp of past data. Some situations where sentiment analysis might fail are:

• Sarcasm, jokes, irony. These things generally don’t follow a fixed set of rules, so they might not be correctly classified by sentiment analytics systems.
• Nuance. Words can have multiple meanings and connotations, which are entirely subject to the context they occur in.
• Multipolarity. When the given text is positive in some parts and negative in others.
• Negation detection. It can be challenging for the machine because the function and the scope of the word ‘not’ in a sentence is not definite; moreover, suffixes and prefixes such as ‘non-,’ ‘dis-,’ ‘-less’ etc. can change the meaning of a text.

7. Key takeaways and next steps

In this article, we examined the science and nuances of sentiment analysis. While sentimental analysis is a method that’s nowhere near perfect, as more data is generated and fed into machines, they will continue to get smarter and improve the accuracy with which they process that data. 

All in all, sentimental analysis has a large use case and is an indispensable tool for companies that hope to leverage the power of data to make optimal decisions.

For those who believe in the power of data science and want to learn more, we recommend taking this free, 5-day introductory course in data analytics . You could also read more about related topics by reading any of the following articles:

• The Best Data Books for Aspiring Data Analysts
• PyTorch vs TensorFlow: What Are They And Which Should You Use?
• These Are the Best Data Bootcamps for Learning Python

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Join the community, add a new evaluation result row, sentiment analysis.

1362 papers with code • 40 benchmarks • 97 datasets

Sentiment Analysis is the task of classifying the polarity of a given text. For instance, a text-based tweet can be categorized into either "positive", "negative", or "neutral". Given the text and accompanying labels, a model can be trained to predict the correct sentiment.

Sentiment Analysis techniques can be categorized into machine learning approaches, lexicon-based approaches, and even hybrid methods. Some subcategories of research in sentiment analysis include: multimodal sentiment analysis, aspect-based sentiment analysis, fine-grained opinion analysis, language specific sentiment analysis.

More recently, deep learning techniques, such as RoBERTa and T5, are used to train high-performing sentiment classifiers that are evaluated using metrics like F1, recall, and precision. To evaluate sentiment analysis systems, benchmark datasets like SST, GLUE, and IMDB movie reviews are used.

Further readings:

• Sentiment Analysis Based on Deep Learning: A Comparative Study

Benchmarks Add a Result

--> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> --> -->

Trend	Dataset	Best Model	Paper	Code	Compare
		T5-11B
		RoBERTa-large with LlamBERT
		Heinsen Routing + RoBERTa Large
		XLNet
		VLAWE
		XLNet
		Bangla-BERT (large)
		MA-BERT
		AnglE-LLaMA-7B
		BERT large
		BERT large
		InstructABSA
		W2V2-L-LL60K (pipeline approach, uses LM)
		BERTweet
		UDALM: Unsupervised Domain Adaptation through Language Modeling
		RoBERTa-large 355M + Entailment as Few-shot Learner
		k-RoBERTa (parallel)
		CalBERT
		LSTMs+CNNs ensemble with multiple conv. ops
		RobBERT v2
		AEN-BERT
		RuBERT-RuSentiment
		xlmindic-base-uniscript
		LSTMs+CNNs ensemble with multiple conv. ops
		FiLM
		Space-XLNet
		fastText, h=10, bigram
		CNN-LSTM
		CNN-LSTM
		Random
		RoBERTa-wwm-ext-large
		RoBERTa-wwm-ext-large
		AraBERTv1
		AraBERTv1
		AraBERTv1
		Naive Bayes
		SVM
		RCNN
		lstm+bert
		CalBERT

Most implemented papers

Bert: pre-training of deep bidirectional transformers for language understanding.

We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.

Convolutional Neural Networks for Sentence Classification

We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vectors for sentence-level classification tasks.

Universal Language Model Fine-tuning for Text Classification

Inductive transfer learning has greatly impacted computer vision, but existing approaches in NLP still require task-specific modifications and training from scratch.

Bag of Tricks for Efficient Text Classification

facebookresearch/fastText • EACL 2017

This paper explores a simple and efficient baseline for text classification.

RoBERTa: A Robustly Optimized BERT Pretraining Approach

Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging.

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP).

A Structured Self-attentive Sentence Embedding

This paper proposes a new model for extracting an interpretable sentence embedding by introducing self-attention.

Deep contextualized word representations

We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e. g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i. e., to model polysemy).

Well-Read Students Learn Better: On the Importance of Pre-training Compact Models

Recent developments in natural language representations have been accompanied by large and expensive models that leverage vast amounts of general-domain text through self-supervised pre-training.

Domain-Adversarial Training of Neural Networks

Our approach is directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on features that cannot discriminate between the training (source) and test (target) domains.

Organic Search & ML Consultant

Sentiment Analysis: A Deep Dive Into the Theory, Methods, and Applications

Sentiment classification is one of the most beginner-friendly problems in data science. That does not make it easy to do, though.

There are various models developed to perform sentiment analysis on datasets. It is important to understand how they came to be and how they function, to ensure that the model you choose is most suited to the data you have at hand.

In this article, I will summarize the theory behind sentiment analysis, explain the rationale behind it, and show some applications of successful sentiment classification. This article is compiled as a result of a  systematic literature review on topic modeling and sentiment analysis  I did.

What is sentiment analysis in NLP?

Sentiment analysis as a subset of Natural Language Processing (NLP).

NLP is a field of research that studies the ability to decode data from natural language using computational means. NLP also examines how this decoded data can be incorporated into machine learning and statistical programming software.

Through NLP, computer programs can perform data analysis using machine learning algorithms on textual data, which is abundant in a public-source format. In addition, they can extract meaning from data, or otherwise — semantics.

The main aim of the NLP field is to bridge the gaps in communication between computer programs and humans. Programs are constantly improved to decode language and speech data into  meaningful semantic insights through processing, analysis, and synthesis .

How can sentiment analysis be defined?

Sentiment analysis is a discipline that  aims to extract qualitative characteristics from user’s text data , such as sentiment, opinions, thoughts, and behavioral intent using natural language processing methods.

As explained in  Zhang et al.’s (2018) study on sentiment analysis , social media texts are particularly useful for sentiment analysis research as they are:

• used to express a standpoint
• filled with subjective text.

Social media texts are defined in academic literature as short-form texts. This type of text is more challenging to do sentiment analysis on, as there is less context for the model to work with. In comparison, sentiment analysis performed on long-form text, such as news articles, is less challenging.

The effectiveness of the sentiment extraction in short-form text relies on the application of  more advanced methodologies , such as deep convolutional neural networks.

Social media data in particular requires  more complex methods  in information retrieval due to the use of creative language, slang, and abbreviations. The presence of these can impact the efficiency of the sentiment analysis model, if not accounted for.

How is sentiment analysis done?

Traditional studies on sentiment analysis aim to  detect polarity in a given text by classifying it as positive, negative, or neutral .

This categorization need is considered one of the  key limitations to traditional sentiment analysis . Some academics argue that sentiment analysis in its traditional form is unable to address the complexities of modern-day expression, as it fails to capture objectivity and subjectivity. For instance, if a model is tasked to categorize between fake news or someone’s opinions and facts, traditional sentiment analysis will not be sufficient.

More advanced methods attempt to  recognize multiple differentiated affective manifestations in text , which indicate emotions and opinions through analysis of the language used for self-expression.

Such methods often aim to simultaneously detect and extract topic models. For this reason, deep learning approaches such as  convolutional neural networks (CNNs)  are often used. CNNs are also used in sentiment analysis of short-form texts, as indicated in multiple research papers (e.g.  Dos Santos and Gatti (2014) ,  Kale et al. (2018) , and  Tang et al. (2015) ).

What models can be used for sentiment analysis?

Sentiment analysis can be done via supervised, semi-supervised, and unsupervised machine learning algorithms.

Supervised machine learning models are  the most difficult to obtain data on  for sentiment analysis, as it requires labels for a subset of the data, with which to train the model.

Semi-supervised approaches utilize a small number of labeled samples as training data as means of improving classification accuracy, with an example being the  model published by da Silva et al. (2016 ), where Twitter data is classified using a Support Vector Machines (SVM) classifier.

What text can sentiment analysis be performed on?

Sentiment analysis can be performed at a  document level, sentence level, and aspect (word) level .

Short-form texts, such as content from social media are best analyzed with sentiment analysis at a sentence level as they usually consist of a single or few sentences.

Some models analyze individual words with the assumption that words in the same sentence share the same emotion. Such an approach is  Tang et al.’s (2019) hidden Topic-Emotion Transition model , which models topics and emotions in successive sentences as a Markov chain.

This approach enables the simultaneous detection of document-level and sentence-level emotion.

Can sentiment analysis be performed on another type of data, besides text?

Multimodal sentiment analysis has grown as a field in recent years , with models proposed in the area taking advantage of recent developments in weakly supervised deep learning approaches.

Multimodal event topic modeling has also emerged, which has been demonstrated as promising for the area of  predictive analysis of consumer behavior and sociology .

Together, topic modeling and sentiment analysis in a multimodal context are recognized as a way of improving human-agent interactions. An example of how this is applied is the area of  automatic speech recognition .

What are the two main ways to do sentiment analysis?

#1 using a pre-developed, manually-built sentiment lexicon..

Sentiment analysis has initially been performed using pre-developed, manually built sentiment lexicons.

Such lexicons are:

• Subjectivity Wordlist
• WordNet-Affec t
• SentiWordNet
• Opinion lexicon

Each of these has a various scale of rating and various word counts.

Such lexicons have been used as foundations for model development, with examples being

• Polarity Classification Algorithm (PCA), which classifies tweet sentiment
• Enhanced Emoticon Classifier (EEC)
• Improved Polarity Classifier (IPC)
• SentiWordNet Classifier (SWNC).

Amongst these, superior performance is demonstrated by the PCA.

Benefits and limitations of this approach

These approaches are useful in:

• distinguishing subjective or objective speech
• categorizing sentiment as positive, negative, or neutral.

As a limitation, they only enable researchers to extract sentiment primarily from the perspective of the writer as opposed to the reader.

#2 Using a Machine Learning Approach

Except for lexicon-based approaches, sentiment analysis can be performed using a machine learning approach.

This can be done via  statistical models trained on human-annotated datasets , hence — utilizing semi-supervised learning.

Another way is to combine multiple shallow machine-learning approaches. For instance, for sentiment analysis of tweets, Ahuja et al. (2019) conclude that TF-IDF performs better as compared to N-Grams in terms of feature extraction. The combination of TF-IDF with logistic regression is considered most efficient among the studies sample of their paper.

Ensemble classifiers are also shown to be a good way to solve one of the limitations of lexicon approaches. Specifically, a  2018 study  approaches the problem of multi-label sentiment classification from the perspective of the reader, applying a model to a news dataset. The study demonstrates the superiority of ensemble classifiers when compared to other methods.

As mentioned previously, deep learning techniques can also be applied in sentiment analysis.

Benefits and Limitations of this approach

Each perspective offers its limitations and opts for compromising either the accuracy or generalisability of the analysis. Here is a  summary of the benefits and limitations of common sentiment analysis machine learning approaches :

• N-gram models (or conditional random fields) work via sequence tagging (e.g. part of speech tagging or shallow parsing). They capture work order and grammar well. Their disadvantage is high feature dimensionality.
• Semi-supervised learning approaches are good for quick and easy determination of the polarity of text. They are not so good at determining subjectivity or objectivity in the text.
• Deep Learning approaches are good at meta-level feature extraction with large datasets and typically perform better than N-gram models. They do not perform well when there is a lot of noise in the data, such as slang, abbreviations, or even emojis. This makes them ill-suited to social media sentiment analysis.
• Multiple kernel learning models work when features are organized into groups. This enables multimodal sentiment analysis, but the computation is slow.

The main challenge in sentiment analysis is language ambiguity or the fact that many times when language is spoken, there might be a presence of mixed semantic attributes. This makes it difficult for a classification algorithm to perform its function.

It is also difficult for people who will view the program’s output and classification to categorize the context in which the semantics are given, which might hinder the effectiveness of the classification overall and its usability in a real-world context.

Even so, it is considered that these hindrances are outweighed by the benefits that sentiment classification offers in terms of speed in comparison with human evaluation and insight.

Why should companies implement sentiment analysis?

There can be many reasons why companies would like to tap into sentiment analysis and natural language processing technologies.

Based on my research, I’ve summarised five main reasons, for which I will also be providing examples of how this is done in real life.

#1 Increase competitive advantage

The implementation of sentiment analysis and predictive behavior modeling techniques is considered a source of competitive advantage for organizations and is  recommended by scholars .

#2 Evaluate the power of a company’s consumer network

Sentiment Analysis can also be used in measuring the power of the consumer’s network. This relates to measuring how efficient word-of-mouth marketing can be.

It can also be used to track individual recommendations given amongst members of online societal groups. This can enable companies to  target consumers with personalized web-ads, based on the recommendation given by their peer s.

#3 Utilize public, user-generated, and readily available data

Textual data is also, more available than numeric and it can be argued that high-level human language contains a huge amount of complexity and nuance.

This has led to sentiment analysis being researched as a potential solution to a variety of business problems in various contexts such as pattern recognition of social media for the detection of road accidents or stock market prediction, to name a few.

#4 Identify patterns and make accurate, data-driven predictions about market changes

Asur and Huberman  argue deep understanding of social media communications can aid accurate predictions of the outcome of future events. The example they provide is looking at real-time analytical software using Twitter feeds data for predicting box-office success. This outperformed Hollywood Stock Exchange information.

Twitter data has also been used for cluster analysis  by a cognitive pattern recognition system , which picked up real-time information on happening road-traffic events prior to any mainstream reporting channels.

Similar studies help affirm that social media platforms are a pool of collective wisdom. Thus, companies are incentivized to apply machine learning for data mining and behavioral prediction to accurately inform future actions of business users for various purposes:

• market prediction  — e.g. stock market
• outcome forecasting — e.g. for  political elections
• crisis  management
• understanding  individual and collective emotional response  communication — a similar approach was implemented in one of  Lidl’s social media campaigns

All of these studies anticipate errors in forecast upon conducting intelligent analytical processes on human emotion, response, and social behavior, due to the unpredictable nature of us, humans, and have noted this as a model limitation.

#5 Efficiency, processing speed, and reduced human errors

Due to the existing constraints of machine learning software in performing text analytics, companies currently have intense human-resource-related spending for staff to go manually validate data. Yet there are even further inefficiencies when humans do these tasks, themselves, mostly related to the time needed to shift through data.

Just imagine how inefficient would it be for anyone to go through thousands of tweets and classify them!

This presents an opportunity to create value by solving existing business problems through continuous improvement of the models, operating in the industry. The implementation of an autonomous solution could reduce the risk of human error in the interpretation of the data.

How to get started with sentiment analysis in Google Sheets ✨

A common misconception is that you need coding skills to start doing sentiment analysis. This couldn’t be further from the truth. You can get started analysing sentiment in text in Google Sheets, using the Google Natural Language API. Use the Google Sheets template linked in my resource section for Sentiment Analysis , for which you will only need an API key and some text to analyze.

To get started, follow these simple steps:

• get your Google Natural Language API key
• copy the Google Sheets template
• Click on the Extensions menu from the top then AppScript, then enter your API key in line 4 of the code. Then click SAVE project
• Return to the sheet. Paste the text data in column A
• Find the menu in the top-nav that says Sentiment Tools > Mark entities and Sentiment and click it.
• Your data will populate in the pink columns and is ready for your to visualize ( check out my Looker Studio dashboard if you need help with the visualization of the data ).

This type of analysis can be very useful, especially in digital marketing and consumer research as it can be used for things like online reputation management, brand sentiment monitoring, or social comments sentiment analysis. To see some practical ways to incorporate sentiment analysis in your digital marketing or SEO strategy, check out this blog post: 7 Practical ways to implement sentiment analysis in digital marketing and SEO .

Final Thoughts

One of the challenges, faced by natural language processing and machine learning, in general, is that useful and problem-relevant information is often seeded in a large pool of chaotically clustered data. Another challenge is that the data might not provide any insight or might be considered useless in relation to the business problem when approached for consideration by a human agent.

These challenges are also reflected in sentiment analysis, with it being a subset of NLP.

This has sparked a wave of research into understanding the constructs of language and mining from it: user intent, sentiment, and subjectivity.

Such models are targets for organizations due to the potential of:

• increased competitive advantage,
• ability to predict behavior and response,
• ability to better target consumers at different stages of their consumer journey
• the potential of reduced need for human involvement, thus greater efficiency, and reduced operational costs.

Through monitoring of public sentiment, companies can become more adaptive to the market.

• 10 practical ways to implement entity analysis in digital marketing and SEO
• 7 Practical ways to implement sentiment analysis in digital marketing and SEO
• 4 ways to boost your SERP competitor analysis with machine learning
• Ultimate End-to-End Guide to Fuzzy Matching For SEOs (with Google Sheets template)
• How to do an SEO Internal Link Audit and Topic Clustering using ML
• Machine Learning for SEO: How to Get Started

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• > Sentiment Analysis
• > Introduction

Book contents

• Sentiment Analysis
• Studies in Natural Language Processing
• Copyright page
• Acknowledgments
• 1 Introduction
• 2 The Problem of Sentiment Analysis
• 3 Document Sentiment Classification
• 4 Sentence Subjectivity and Sentiment Classification
• 5 Aspect Sentiment Classification
• 6 Aspect and Entity Extraction
• 7 Sentiment Lexicon Generation
• 8 Analysis of Comparative Opinions
• 9 Opinion Summarization and Search
• 10 Analysis of Debates and Comments
• 11 Mining Intent
• 12 Detecting Fake or Deceptive Opinions
• 13 Quality of Reviews
• 14 Conclusion
• Bibliography

1 - Introduction

Published online by Cambridge University Press:  23 September 2020

Sentiment analysis, also called opinion mining, is the field of study that analyzes people’s opinions, sentiments, appraisals, attitudes, and emotions toward entities and their attributes expressed in written text. The entities can be products, services, organizations, individuals, events, issues, or topics. The field represents a large problem space. Many related names and slightly different tasks – for example, sentiment analysis, opinion mining, opinion analysis, opinion extraction, sentiment mining, subjectivity analysis, affect analysis, emotion analysis, and review mining – are now all under the umbrella of sentiment analysis. The term sentiment analysis perhaps first appeared in Nasukawa and Yi (2003), and the term opinion mining first appeared in Dave et al. (2003). However, research on sentiment and opinion began earlier (Wiebe, 2000; Das and Chen, 2001; Tong, 2001; Morinaga et al., 2002; Pang et al., 2002; Turney, 2002). Even earlier related work includes interpretation of metaphors; extraction of sentiment adjectives; affective computing; and subjectivity analysis, viewpoints, and affects (Wiebe, 1990, 1994; Hearst, 1992; Hatzivassiloglou and McKeown, 1997; Picard, 1997; Wiebe et al., 1999). An early patent on text classification included sentiment, appropriateness, humor, and many other concepts as possible class labels (Elkan, 2001).

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• Introduction
• Bing Liu , University of Illinois, Chicago
• Book: Sentiment Analysis
• Online publication: 23 September 2020
• Chapter DOI: https://doi.org/10.1017/9781108639286.002

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What is sentiment analysis?

What is sentiment analysis used for, why is sentiment analysis important, use cases for sentiment analysis, types of sentiment analysis, pros and cons of sentiment analysis, how does sentiment analysis work, sentiment analysis challenges, 3 places to analyse customer sentiment, sentiment analysis tools, try qualtrics for free, what is sentiment analysis and how to use it.

21 min read Survey results, customer reviews, social media mentions, oh my. It’s a feedback-driven world, and our brands are just living in it. But how can you turn all of that data into meaningful insights? Find out how sentiment analysis can help.

The days of relying on a great product or service to do your branding are behind us, which is why  acquiring all types of feedback  is important.

Quantitative feedback like  net promoter scores  can provide a general pulse of your brand performance, but qualitative feedback in the form of text can provide insight into how people actually “feel” about your brand.

Sifting through textual data, however, can be impossibly time-consuming for some brands. Doing so manually just isn’t feasible and the nuances of brand sentiment could be difficult to capture. Whether analysing solicited feedback via channels such as surveys or examining unsolicited feedback found on social media, online forums and more, it’s impossible to comprehensively identify and integrate data on brand sentiment when relying solely on manual processes.

One solution to this problem? Sentiment analysis.

Let’s look at the importance of sentiment analysis and how it can be used to improve customer experience through direct and indirect interactions with your brand. Here’s an introduction to sentiment analysis, how it works and when to use it.

Sentiment analysis definition:  sentiment analysis is the process of determining the opinion, judgment or emotion behind natural language. Sentiment analysis provides an effective way to evaluate written or spoken language to determine if the expression is favorable, unfavorable, or neutral, and to what degree. Because of this it gives a useful indication about how the customer felt about their experience.

If you’ve ever left an online review, made a comment about a brand or product online, or answered a large-scale  market research survey , there’s a chance your responses have been through sentiment analysis.

The opinion you expressed by typing into a text box would be turned into categorical data (like “positive”, “negative” or “neutral”), added to data from many other people’s comments, and summarised to give a business a bird’s eye view of how the general public responded to their brand or product.

Sentiment analysis is part of the greater umbrella of text mining, also known as text analysis . This type of analysis extracts meaning from many sources of text, like  surveys , reviews, public social media, and even articles on the Web. A score is then applied based on the sentiment of the text. For example,  -1  for negative sentiment and  +1  for positive sentiment. This is done using natural language processing (NLP).

Today’s algorithm-based sentiment analysis tools can handle huge volumes of customer feedback consistently and accurately. A type of text analysis, sentiment analysis, reveals how positive or negative customers feel about topics ranging from your products and services to your location, your advertisements, or even your competitors.

Automatically uncover trends, problems and opportunities with TextiQ

So if sentiment analysis is hard to do, what’s the benefit? Why do we use  tools to categorise natural language feedback  rather than our human brains?

Mostly, it’s a question of scale. Sentiment analysis is helpful when you have a large volume of text-based information that you need to generalise from.

For example, let’s say you’re in marketing for a major motion picture studio, and you just released a trailer for a movie and got a huge volume of comments about it on Twitter.

You can read some – or even a lot – of the comments, but you won’t be able to get an accurate picture of how many people liked or disliked it unless you look at every last one and make a note of whether it was positive, negative or neutral. That would be prohibitively expensive and time-consuming, and the results would be prone to a degree of human error.

On top of that, you’d have a risk of bias coming from the person or people going through the comments. They might have certain views or perceptions that color the way they interpret the data, and their judgment may change from time to time depending on their mood, energy levels and other normal human variations.

With sentiment analysis tools though, you can get a comprehensive, consistent overall verdict with a simple button-press.

Sentiment scores help businesses understand  what sort of emotions their brand evokes  in a group of people. These emotions can be happiness, sadness, anger, or simply impartialness. From there, it’s up to the business to determine how they’ll put that sentiment into action. One way is to let sentiment inform how customers are currently experiencing your brand.

Sentiment analysis is critical because it helps provide insight into how customers perceive your brand.

Customer feedback – whether that’s via social media, the website, conversations with service agents, or any other source – contains a treasure trove of useful business information, but it isn’t enough to know what customers are talking about. Knowing how they feel will give you the most insight into how their experience was. Sentiment analysis is one way to understand those experiences.

Sometimes known as “opinion mining,” sentiment analysis can let you know if there has been a change in public opinion toward any aspect of your business. Peaks or valleys in sentiment scores give you a place to start if you want to make product improvements, train sales reps or customer care agents, or create new marketing campaigns.

We live in a world where huge amounts of written information is produced and published every moment, thanks to the internet, news articles, social media and digital communications. Sentiment analysis can help companies keep track of how their brands and products are perceived, both at key moments and over a period of time.

It’s commonly used in  market research , PR, marketing analysis,  reputation management , stock analysis and financial trading,  customer experience , product design and many more fields besides.

Here are a few scenarios where sentiment analysis can save time and add value:

• Social media listening  – in day-to-day monitoring, or around a specific event such as a product launch
• Analysing survey responses  for a large-scale research program
• Processing employee feedback in a large organisation
• Identifying very unhappy customers  so you can  offer closed-loop follow up
• See where sentiment trends are clustered  in particular groups or regions
• Competitor research  – checking your approval levels against comparable businesses

Not all sentiment analysis is done the same way. There are different ways to approach it and a range of different algorithms and processes that can be used to do the job, depending on the context of use and the desired outcome.

Basic sub-types of sentiment analysis include:

• Detecting sentiment This means parsing through text and sorting opinionated data (such as “I love this!”) from objective data (like “the restaurant is located downtown”).
• Categorising sentiment This means detecting whether the sentiment is positive, negative or neutral. Your tools may also add weighting to these categories, e.g very positive, positive, neutral, somewhat negative, negative.
• Clause-level analysis Sometimes, text contains mixed or ambivalent opinions, for example “staff were very friendly but we waited too long to be served”. Being able to score feedback at the clause level indicates when there are both good and bad opinions expressed in one place, and can be useful in case the positives and negatives within a text cancel each other out and return a misleading “neutral” result.

In addition, you can choose whether to view the results of sentiment analysis:

• Document level (useful for professional reviews or press coverage)
• Sentence level (for short comments and evaluations)
• Sub-sentence level (for picking out the meaning in phrases or short clauses within a sentence

Sentiment analysis is a powerful tool that offers a number of advantages, but like any research method has some limitations.

Advantages of sentiment analysis:

• Accurate, unbiased results
• Enhanced insights
• More time and energy available for staff do to higher-level tasks
• Consistent measures you can use to track sentiment over time

Disadvantages of sentiment analysis:

• Best for large and numerous data sets. To get real value out of sentiment analysis tools, you need to be analyzing large quantities of textual data on a regular basis.
• Sentiment analysis is still a developing field, and the results are not always perfect. You may still need to sense-check and manually correct results occasionally.

Sentiment analysis uses machine learning, statistics and natural language processing (NLP) to find out how people think and feel on a macro scale. Sentiment analysis tools take written content and processes it to unearth the positivity or negativity of the expression.

This is done in a couple of ways:

• Rule-based sentiment analysis This method uses a lexicon, or word-list, where each word is given a score for sentiment, for example “great” = 0.9, “lame” = -0.7, “okay” = 0.1 Sentences are assessed for overall positivity or negativity using these weightings. Rule-based systems usually require additional finessing to account for sarcasm, idioms and other verbal anomalies.
• Machine learning based sentiment analysis A computer model is given a training set of natural language feedback, manually tagged with sentiment labels. It learns which words and phrases have a positive sentiment or a negative sentiment. Once trained, it can then be used on new data sets. In some cases, the best results come from combining the two methods.

Developing sentiment analysis tools is technically an impressive feat, since human language is grammatically intricate, heavily context-dependent, and varies a lot from person to person. If you say “I loved it,” another person might say “I’ve never seen better,” or “Leaves its rivals in the dust”. The challenge for an AI tool is to recognise that all these sentences mean the same thing.

Another challenge is to decide how language is interpreted since this is very subjective and varies between individuals. What sounds positive to one person might sound negative or even neutral to someone else. In designing algorithms for sentiment analysis, data scientists must think creatively in order to build useful and reliable tools.

Getting the correct sentiment classification

Sentiment classification requires your sentiment analysis tools to be sophisticated enough to understand not only when a data snippet is positive or negative, but how to extrapolate sentiment even when both positive and negative words are used. On top of that, it needs to be able to understand the context and complications such as sarcasm or irony.

Human beings are complicated, and how we express ourselves can be similarly complex. Many types of sentiment analysis tools use a simple view of polarity (positive/neutral/negative), which means much of the meaning behind the data is lost.

Let’s see an example:

“I hated the setup process, but the product was easy to use so in the end I think my purchase was worth it.”

A less sophisticated sentiment analysis tool might see the sentiment expressed here as “neutral” because the positive – “the product was easy to use so in the end I think my purchase was worth it” – and negative-tagged sentiments – “I hated the setup process” – cancel each other out.

However, polarity isn’t so cut-and-dry as being one or the other here. The final part – “in the end, I think my purchase was worth it” – means that as a human analysing the text, we can see that generally this customer felt mostly positive about the experience. That’s why a scale from positive to negative is needed, and why a sentiment analysis tool adds weighting along a scale of 1-11.

Scores are assigned with attention to grammar, context, industry, and source, and Qualtrics gives users the ability to adjust the sentiment scores to be even more business-specific.

Better understand your customers with real time sentiment analysis

Understanding context

Context is key for a sentiment analysis model to be correct. This means you need to make sure that your sentiment scoring tool not only knows that “happy” is positive—and that “not happy” is not, but understands that certain words that are context-dependent are viewed correctly.

As human beings, we know customers are pleased when they mention how “thin” their new laptop is, but that they’re complaining when they talk about the “thin” walls in your hotel. We understand that context.

Obviously, a tool that flags “thin” as negative sentiment in all circumstances is going to lose accuracy in its sentiment scores. The context is important.

This is where training natural language processing (NLP) algorithms comes in. Natural language processing is a way of mimicking the human understanding of language, meaning context becomes more readily understood by your sentiment analysis tool.

Sentiment analysis algorithms are trained using this system over time, using deep learning to understand instances with context and apply that learning to future data. This is why a sophisticated sentiment analysis tool can help you to not only analyze vast volumes of data more quickly, but also discern what context is common or important to your customers.

In a world of endless opinions on the Web, how people “feel” about your brand can be important for measuring the customer experience .

Consumers desire likeable brands that understand them; brands that provide memorable on-and-offline experiences. The more in-tune a consumer feels with your brand, the more likely they’ll share their emotions in written text (through surveys, reviews, social media, and more).

But the opposite is true as well. As a matter of fact,  71 percent  of Twitter users will take to the social media platform to voice their frustrations with a brand. Those users also expect brands to respond to queries within an hour of tweeting, but we’ll save that for another day.

These conversations, both positive and negative, should be captured and analysed to improve the customer experience. Sentiment analysis can help.

Let’s first look at how more direct interactions with brands can be analysed.

1. Text analysis for surveys

Surveys are a great way to connect with customers directly, but they’re also ripe with constructive feedback. The feedback within your survey responses can be quickly analysed for sentiment scores.

For the survey itself, consider questions that will generate qualitative customer experience metrics, some examples include:

• What was your most recent experience like?
• How much better (or worse) was your experience compared to your expectations?
• What is something you would have changed about your experience?

Remember, the goal here is to acquire honest textual responses from your customers so the sentiment within them can be analysed. Another tip is to avoid close-ended questions that only generate “yes” or “no” responses. These types of questions won’t serve your analysis well.

Next, use a text analysis tool to break down the nuances of the responses.  TextiQ  is an example of a tool that will not only provide sentiment scores but extract key themes from the responses.

After the sentiment is scored from survey responses, you’ll be able to address some of the more immediate concerns your customers have during their experiences.

Another great way to acquire sentiment is through customer reviews. This method is a bit more indirect compared to surveys.

2. Text analysis for customer reviews

Did you know that 72 percent of customers will not take action until they’ve read reviews on a product or service? An astonishing  95 percent  of customers read reviews prior to making a purchase. In today’s feedback-driven world, the power of customer reviews and peer insight is undeniable.

Review sites like  G2 are common first-stops for customers looking for honest feedback on products and services. This feedback, like that in surveys, can be analysed for emotional responses.

The benefit of customers providing reviews compared to responding to surveys is that it’s more indirect, which  could lead to more honest and in-depth feedback. This isn’t a rule-of-thumb, but analysing customer reviews has its clear upsides.

To improve the customer experience, you can take the sentiment scores from customer reviews – positive, negative, and neutral – and identify gaps and pain-points that may have not been addressed in the surveys. Remember, negative feedback is just as (if not more) beneficial to your business than positive feedback.

3. Text analysis for social media

One of the most indirect ways to acquire textual data is through social media mining. This is made possible using social media management software with monitoring capabilities.

Monitoring tools essentially scrape public social media like Twitter and Facebook for brand mentions and assign sentiment scores accordingly. This has its upsides as well, considering users are highly likely to take their uninhibited feedback to social media.

One downside to text analysis for social media is character limitations. Whereas in surveys and review sites there is a set of contexts, social media is more free reign. This could create some noise within the data, but this isn’t a huge concern.

Regardless, a staggering  70 percent of brands don’t bother with feedback on social media. Because social media is an ocean of big data just waiting to be analysed, brands could be missing out on some important sentiment.

When choosing sentiment analysis technologies, bear in mind how you will use them. There are a number of options out there, from open-source solutions to in-built features within social listening tools. Some of them are limited in scope, while others are more powerful but require a high level of user knowledge.

Text iQ is a natural language processing tool within the Experience Management Platform™ that allows you to carry out sentiment analysis online using just your browser. It’s fully integrated, meaning that you can view and analyse your sentiment analysis results in the context of other data and metrics, including those from third-party platforms.

And like all Qualtrics tools, it’s designed to be straightforward, clear and accessible to those without specialised skills or experience, so there’s no barrier between you and the results you want to achieve.

Analysing customer sentiment, creating better experiences

Whether you’re using a text analysis tool for survey responses or a social media management tool for mining purposes, the key here is to be on the lookout for customer feedback. Sentiment analysis is not a one and done effort and requires continuous monitoring. By reviewing your customers’ feedback on your business regularly, you can proactive get ahead of emerging trends and fix problems before it’s too late.

Acquiring feedback and analysing its sentiment can provide businesses with a deep pulse on how customers truly “feel” about their brand. When you’re able to understand your customers emotionally, you’re able to provide a more robust customer experience.

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• Open access
• Published: 04 September 2024

Advancing sentiment classification through a population game model approach

• Neha Punetha   ORCID: orcid.org/0000-0001-8173-4003 1 &
• Goonjan Jain   ORCID: orcid.org/0000-0001-8499-4433 1  

Scientific Reports volume  14 , Article number:  20540 ( 2024 ) Cite this article

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• Computer science
• Information technology
• Mathematics and computing
• Scientific data

Computational Sentiment Analysis involves the automation of human emotion comprehension by categorizing sentiments as positive, negative, or neutral. In the contemporary digital environment, the extensive volume of social media content presents significant challenges for manual analysis, thereby necessitating the development and implementation of automated analytical tools. To address the limitations of existing techniques, which heavily rely on machine learning and extensive dataset pre-training, we propose an innovative unsupervised approach for sentiment classification. This novel methodology is grounded in game theory concepts, particularly the population game model, offering a promising solution by circumventing the need for extensive training procedures. We extract two textual features from review comments, namely context score and emotion score. Leveraging lexicon databases and numeric scores, this cognitive mathematical framework is language-independent. Competitive results are demonstrated across various domains (hotels, restaurants, electronic devices, etc.), and the efficacy of the proposed work is validated in two languages (English and Hindi). The highest accuracy recorded for the English domain dataset is 89%, while electronic Hindi reviews attain an 84% accuracy rate. The proposed model exhibits domain and language independence, validated through statistical analyses confirming the significance of the findings. The framework demonstrates noteworthy rationality and coherence in its outcomes.

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Introduction.

In the expanding digital ecosystem, where text is continuously generated across diverse platforms and devices, the task of accurately interpreting the sentiments embedded within the text has become a critical area of study. Computational Sentiment Analysis, an integral subfield of Natural Language Processing (NLP), addresses this challenge by automating the complex process of analyzing human sentiment conveyed through language. This task seeks to elucidate the intricate layers of emotions, opinions, and attitudes present in textual data, which has wide-ranging applications in numerous fields 1 . The vast and growing volume of online content—from consumer reviews to social media posts—renders manual analysis impractical and often infeasible. As a result, there is an increasing demand for automated sentiment analysis tools that can handle extensive datasets efficiently. Various sophisticated algorithms have been developed to fulfill this need, playing a crucial role in deriving actionable insights from textual data. Such advancements are essential for enabling organizations to make data-driven decisions and sustain a competitive edge in today’s digital landscape 2 .

For the extraction of sentiment from text, three types of approaches are most commonly used in the literature: lexicon-based approaches, machine-learning approaches, and hybrid approaches. Lexicons are predefined dictionaries of words or phrases with associated sentiment scores. However, these lexicons are not exhaustive and may not include all the words or expressions in a given domain or language 3 . This can lead to inaccurate sentiment analysis results, particularly for newer or domain-specific terms. In machine learning approaches, one major drawback is their dependence on high-quality, labeled training data. Acquiring and annotating large amounts of data can be time-consuming, expensive, and subjective 2 . Additionally, machine learning models can suffer from overfitting and fail to generalize well to new or unseen data. Furthermore, adapting models to new domains or handling noisy and unstructured data can pose significant challenges. Hybrid approaches in sentiment analysis, which combine multiple techniques such as lexicons, machine learning, and rule-based systems, also have certain drawbacks 4 . One drawback is increased complexity and computational overhead. Integrating different approaches and coordinating their interactions can be challenging, requiring additional effort in feature extraction, model design, and optimization 5 . Another drawback is the potential for conflicting results or errors when different techniques yield contrasting sentiment predictions. Combining disparate methods may introduce additional sources of noise and ambiguity, leading to decreased accuracy and reliability. Furthermore, hybrid approaches may require domain-specific knowledge and expertise to effectively select, combine, and fine-tune the various components. Overall, while hybrid approaches have the potential to leverage the strengths of multiple techniques, careful consideration, and rigorous evaluation are necessary to ensure their effectiveness and mitigate their drawbacks 6 . To overcome the limitations associated with traditional methods, we present a novel approach for sentiment analysis at the sentence level in review datasets. The primary goal is to develop a decision-making system capable of effectively determining the sentiment of online reviews. The main contributions of this study are as follows:

Innovative mathematical sentiment analysis approach: This study presents a groundbreaking unsupervised sentiment classification model based on population game theory. Unlike traditional models, it does not require labelled datasets, making it highly adaptable and reducing the dependency on extensive pre-existing data. This innovation addresses a significant limitation in sentiment analysis by offering a more flexible and scalable solution.

Dynamic and real-time sentiment tagging: The population game model dynamically adapts to changing language patterns and sentiment expressions over time. By framing sentiment categories as players and sentiment labels as strategies, the model achieves a Nash equilibrium that ensures stable and accurate sentiment classification. This dynamic adaptation is crucial for maintaining model effectiveness in real-world applications where language and context continuously evolve.

Cross-language and cross-domain robustness: The model’s design emphasizes language and domain independence, proven effective across various datasets, including English and Hindi reviews from multiple domains like hotels, restaurants, and electronic devices. This broad applicability highlights the model’s robustness and potential for wide-scale deployment in diverse sentiment analysis tasks.

Enhanced accuracy through strategic interaction modelling: By capturing the strategic interactions between context and emotion within an asymmetric game framework, the model provides a more nuanced understanding of sentiment dynamics. This leads to enhanced accuracy, with the study achieving high-performance metrics (89% for English and 84% for Hindi), demonstrating the model’s superiority over existing sentiment analysis techniques.

The study is structured as follows: Section “ Related work ” offers a concise review of previous scientific work in the realm of sentiment analysis. Section “ Methodology ” elaborates on the core tenets of the population game theory model and the proposed algorithmic approach for sentiment analysis. In Section “ Result and evaluation ”, we assess the efficacy of the proposed model by juxtaposing it against alternative methods across different review datasets. This evaluation is substantiated through adherence to specified guidelines and statistical significance tests, accompanied by a thorough analysis of the algorithm's strengths and limitations. Section “ Discussion .” encapsulates the error rate, significance, real-life implementation, and limitations of the study and delineates potential avenues for future research. Finally, Section “ Conclusion ” presents the conclusion and the future work.

Related work

Textual sentiment analysis methods include Topic-based approaches, machine learning approaches, deep learning approaches, and mathematical modeling approaches. Each of these methods has its strengths and limitations, and understanding these can help identify the gap that our proposed work aims to fill as illustrated in Table 1 .

Addressing research gap

Below are some of the research gaps that we identified by analyzing different approaches, mentioned in Table 1 .

Topic modeling: Topic modeling methods, such as (W2VLDA), often lack contextual understanding, relying on word co-occurrence without grasping the meaning, which can result in incoherent topics. The need to predefine the number of topics can be challenging, and the models are computationally intensive and sensitive to hyperparameters. They also ignore word order, affect meaning, and may struggle with short texts. Additionally, interpretability and pre-processing quality can impact their effectiveness.

Machine learning: Machine learning methods, such as Lagrangian Support Vector Machine (LSVM) and Bidirectional Neural Networks, face major challenges while handling high-dimensional data, particularly, overfitting, especially with limited training data. Recurrent neural network (RNN)-based attention models are particularly prone to overfitting. Ontology embeddings struggle with polysemy and ambiguity in domain-specific languages. Selective Domain Adaptation (SDA) encounters difficulties in managing domain shifts while maintaining model interpretability. Keyphrase extraction methods using fuzzy entropy and k-means clustering, which rely on predefined algorithms, may miss emerging sentiment patterns. Furthermore, methods like BERT Post Training (BERT-PT) have high demands for computational resources and labeled data for fine-tuning.

Deep learning: Deep learning models, including BERT and Attention-Based Point Networks (AttPNet), have significant drawbacks due to their substantial computational resource requirements and the complexity involved in their deployment. Techniques such as Recurrent Memory Neural Networks (ReMemNN) experience performance degradation when dealing with noisy or sparse data. BERT with knowledge incorporation demands extensive computational power and pre-training. Syntactic and semantic analysis approaches struggle with scalability due to high computational demands. BERT-Multi Layer Convolutional Neural Network (B-MLCNN) often overlooks nuanced sentiment expressions within sentences or phrases.

Mathematical approaches: Existing mathematical models for sentiment analysis, such as SA-MpMcDM, face drawbacks due to the need for extensive customization across different review platforms, which limits their adaptability. Bayesian game models based approaches are dependent on the probability of the occurrence of textual features. Various MCDM-based frameworks for sentiment classification are challenged by their reliance on weights and the extensive customization required.

In summary, the proposed method fills these research gaps by offering consistent performance across domains, effective handling of high-dimensional data, simplified deployment, and adaptability to language trends and domain shifts, thus ensuring scalability and robustness in sentiment analysis. These contributions aim to enhance the reliability, applicability, and practicality of sentiment analysis methodologies in real-world scenarios.

Methodology

The utilization of the population game models for sentiment analysis is given in Section “ Population game model ”. Section “ Proposed methodology ” contains the proposed methodology for sentiment analysis. We give a detailed illustrative example of the proposed model in Section “ Numerical illustration ”.

• Population game model

The population game model is a mathematical framework that integrates concepts from game theory and evolutionary biology to analyze the dynamics of strategic interactions within a population 26 . The population game model offers a compelling framework for sentiment analysis due to its alignment with the dynamics of strategic interactions inherent in sentiment classification.

Sentiment analysis inherently involves strategic interactions between different sentiment categories or strategies. By framing sentiment categories as players and sentiment labels as strategies within the population game model, we accurately capture the strategic nature of sentiment classification. This representation allows for a nuanced understanding of how different sentiment strategies compete and evolve. As a first step, the model defines the “players” as various elements contributing to sentiment, such as context and emotion, while sentiment categories like positive and negative sentiments are regarded as distinct “strategies.” The performance of each strategy is then quantified through a payoff matrix, indicating how well the strategies predict sentiment in given text data. From there, the model calculates the fitness of each sentiment category based on its predictive capability in the given text data. This fitness assessment forms the basis for determining the dynamic changes in strategy proportions over time. Utilizing replicator dynamics, the model describes how the proportions of different sentiment strategies evolve. This dynamic process ensures that the sentiment analysis model adapts and optimizes its strategies over time in response to changing language patterns, sentiment expressions, and user preferences. As the proportions of sentiment categories evolve, the model reaches a steady state, representing a Nash equilibrium where the distribution of sentiment categories stabilizes. At this point, no single sentiment category can unilaterally improve its performance, indicating a convergence to a stable state where the sentiment analysis model maintains a consistent distribution of sentiment categories. Throughout this iterative process, the population game model enables continuous adaptation and optimization of sentiment analysis algorithms. By updating strategy proportions according to replicator dynamics, the model ensures adaptability to changes in sentiment trends and linguistic patterns, ultimately leading to improved performance and adaptability in real-world applications. Evaluation and validation of the model's performance at this steady state help ensure its effectiveness in accurately capturing sentiment in text data.

Overall, the population game model offers a suitable theoretical framework for sentiment classification due to its ability to capture the dynamic nature of sentiment analysis, optimize sentiment analysis algorithms, achieve stable equilibrium, and facilitate systematic evaluation and validation. By leveraging the strategic interactions inherent in sentiment classification, this model enhances the accuracy, adaptability, and performance of sentiment analysis systems in various real-world applications.

We employ the population game model to accurately reflect the diverse and varying influences of context and emotion on strategic interactions. Unlike symmetric games, where all players have identical strategies and payoffs, asymmetric games allow us to capture the inherent differences between the players' roles and payoffs in our model. This distinction is crucial because the strategies and outcomes for context and emotion are not identical and affect each other differently. By employing an asymmetric game framework, we can more precisely model and analyze the dynamics of how context and emotion interact, evolve, and reach equilibrium, leading to a more realistic and applicable understanding of the strategic behavior within the population.

A population game model is represented by a tuple G  =  < N , S , A , B , p , q , F 1, F 2 , E 1 , E 2 , \(\dot{p}\) , \(\dot{q}\) > where each component is defined as follows.

Players ( N ): The model consists of N players. In the specific context of this task, there are two players: context ( C ) and emotion ( E ) for sentiment analysis.

Strategies ( S ): The strategies available to players. In the proposed work, we have two strategies viz., positive and negative.

Payoff matrices ( A and B ):

Matrix A represents the payoffs for player 1 (context). It is denoted by A = [ a ij ], where a ij represents the payoff for context playing strategy i against emotion playing strategy j .

Matrix B represents the payoffs for player 2 (emotion). It is denoted by B = [ b ij ], where b ij represents the payoff for emotion playing strategy j against context playing strategy i .

Strategy proportion ( p and q ):

p : A vector of proportions for Player 1 (context). Since we use only two strategies, thus this vector is of size two (02). The individual values are referred to as p 1 and p 2 . p 1 is the proportion of context choosing the positive strategy and p 2 is the proportion of context choosing the negative strategy. The additional constraint is: p 1  +  p 2  = 1 with p 1 , p 2  ≥  0.

q : A vector of proportions for Player 2 (emotion). Similarly, q 1 is the proportion of emotion choosing the positive strategy and q 2 is the proportion of emotion choosing the negative strategy. The constraint is q 1  +  q 2  = 1 with q 1 , q 2  ≥ 0.

Combined strategy proportion ( \(\pi\) ): A combined vector representing the strategy proportions of both players.

where \({\pi }_{1}= \left[\begin{array}{c}{p}_{1}\\ {p}_{2}\end{array}\right]\) represents the strategy proportions of player 1 and \({\pi }_{2}= \left[\begin{array}{c}{q}_{1}\\ {q}_{2}\end{array}\right]\) represents the strategy proportion of player 2.

Fitness functions ( F 1 and F 2 ):

The fitness of individual players in the population game model is calculated to understand and analyze the dynamics of the population and how different strategies contribute to the overall success of the individual players. It helps to understand which strategies are more effective in maximizing payoffs and providing individuals with a competitive advantage. For player 1 (context), the fitness ( F 1 ( π , i )) is calculated using Eq. ( 1 ). Equation ( 2 ) shows the equation to calculate the fitness \({F}_{2}\left({\pi }_{2}, j\right)\) of player 2 (emotion).

The fitness of players helps us to understand which strategies are more effective in maximizing payoffs and providing players with a competitive advantage.

Expected fitness ( E 1 and E 2 ):

Expected fitness is the average expected payoff of individual players in the population, considering the proportions of different strategies. It is calculated by taking the weighted average of expected payoffs for each individual, where the weights are given by the proportions of strategies in the population. Equations ( 3 ) and ( 4 ) show the formulas to calculate the expected fitness of the two players – context and emotion, respectively.

Replicator dynamics ( \(\dot{{p}_{i}}\) and \(\dot{{q}_{i}}\) ):

The rate of change of strategies concerning time t is known as replicator dynamics 27 . It can be represented as a set of differential equations where the rate of change of the proportion of strategy j is determined by the difference between the fitness of strategy i and the average fitness of all strategies in the population. The proportions of strategies in the population can change over time according to replicator dynamics, which describe how the proportions evolve based on the fitness of different strategies. The rate of change of strategy proportion over time for player 1 (context) is given by Eq. ( 5 ). For player 2 (emotion), the rate of change of strategy proportion over time is given by Eq. ( 6 ).

Steady-state:

The steady state is reached when the proportions of strategies no longer change over time. At this point, the model reaches a Nash equilibrium, where no player can improve their payoffs by unilaterally changing their strategy. In other words, the sentiment analysis model converges to a stable state where the distribution of sentiment categories remains constant. Mathematically, this is represented by Eqs. ( 7 ) and ( 8 ) for player 1 (context) and player 2 (emotion), respectively.

Additionally, the sum of all strategy proportions in the population must be 1 as given by Eqs. ( 9 ) and ( 10 ) for context and emotion, respectively.

Note on time dependence : The strategy proportions p i  and q j  are explicit functions of time t . This means that the proportions of strategies for both players are not static but can change over time. The dynamics of these proportions are governed by the replicator equations, reflecting how the fitness and relative success of strategies drive the evolution of these proportions in the population. As the game progresses, these proportions adjust until they reach a steady state where they remain constant, indicating that the population has reached a Nash equilibrium.

Proposed methodology

The proposed methodology comprises of two phases—Phase I and Phase II. Figure  1 outlines the flowchart delineating the two-phase framework. Phase I involves the extraction of textual features from the review comments, utilizing suitable algorithms. Subsequently, we generate a decision matrix, facilitating further analysis. In Phase II, a population game model is implemented, tailored to the task of sentiment classification. Further elaboration on the different phases is provided in subsequent subsections. Table 2 lists the notations employed in the study.

Flowchart illustrating the structure of the proposed framework.

Phase I: features extraction

Phase I of the methodology is initiated by following a cleaning process to enhance the quality and uniformity of the dataset. This cleaning process encompasses various pre-processing steps, including tokenization, lemmatization, and stop word removal, as depicted in Fig.  2 . This pre-processing ensures that the data is appropriately structured and prepared for subsequent analytical procedures. After the cleaning process, the focus shifts towards the extraction of textual features from the refined dataset. The next step entails the retrieval of two distinct features from the text, namely context scores (( \(PCS^{ \otimes }\) ), ( \(NCS^{ \otimes }\) )) and emotion scores (( \(PES^{ \otimes }\) ), ( \(NES^{ \otimes }\) )), as delineated in Fig.  2 . The concluding step of this phase involves the generation of a decision matrix of order 2 × 2. This matrix is the normal form representation of the game consisting of two players (context and emotion), two strategies (positive and negative), and payoffs of the players (( \(PCS^{ \otimes }\) ), ( \(NCS^{ \otimes }\) ), ( \(PES^{ \otimes }\) ) and ( \(NES^{ \otimes }\) )) for playing certain strategies. Detailed explanations of these payoff computations are explained in step 1 and step 2 below.

Flowchart depicting phase I of the framework.

Step 1 : Evaluate the normalized context scores of reviews (( \(PCS^{ \otimes }\) ), ( \(NCS^{ \otimes }\) ))

Context scores are objective numerical measures allocated to assess the contextual sentiment in a given text, effectively quantifying the magnitude of positive or negative sentiment conveyed within. These scores are calculated utilizing the Python-based libraries, leveraging the capabilities of SentiWordnet ( SWN ) for accurate computation. The resulting context score values reside within the closed interval [0, 1]. ( \(PCS^{ \otimes }\) ) denotes the normalized positive context score and ( \(NCS^{ \otimes }\) ) denotes the normalized negative context score. Algorithm 1 is employed to calculate these scores.

Retrieve textual Context scores

Step 2 : Evaluate the normalized emotion scores of reviews (( \(PES^{ \otimes }\) ), ( \(NES^{ \otimes }\) ))

For this study, emotions are categorized into five distinct categories viz., happy ( H ), angry ( A ), sad ( S ), surprised ( S p ), and fear. However, the emotion of fear is not taken into account. To quantify the emotions, the text2emotion library in Python is utilized. This library enables the computation of emotion scores, which provide numerical representations of the intensity or prevalence of the identified emotions in the text. After obtaining the emotion scores for the five emotions, these emotions are further categorized into two groups: Normalized positive emotion ( \(PES^{ \otimes }\) ) and normalized negative emotion ( \(NES^{ \otimes }\) ). The categorization of emotions into these two categories is performed following Algorithm 2. The resulting values of ( \(PES^{ \otimes }\) ) and ( \(NES^{ \otimes }\) ), representing the degree of positive and negative emotions, respectively, fall within the range of 0–1.

Retrieve textual Emotion scores

Table 3 contains the information about game components i.e. two players (context and emotion) having two strategies (positive and negative) and their respective numeric values ( \(PCS^{ \otimes }\)  =  c + , \(PES^{ \otimes }\)  =  c − , \(NCS^{ \otimes }\)  =  e + and \(NES^{ \otimes }\)  =  e − ). Integrating context and emotion features leads to a more holistic sentiment analysis, offering a comprehensive view by combining the specific details (context) with the intensity and nature of the sentiment (emotion). This integration resolves ambiguities where one feature alone might be misleading, ensuring accurate sentiment classification even in mixed or contradictory scenarios. For instance, a review mentioning "excellent battery life" (positive context) with "I am extremely happy with my purchase" (positive emotion) solidifies the positive sentiment, while a contextually positive statement with a negative emotional tone indicates a complex sentiment that requires deeper analysis. By considering both the rationale (context) and the emotional response (emotion), the proposed methodology ensures accurate sentiment classification, leveraging the strengths of both features to enhance the model's efficiency and reliability in sentiment tagging. This comprehensive approach improves the accuracy of predictions and the robustness of the analysis, making it essential for applications in natural language processing, customer feedback analysis, and automated review systems, where understanding the full spectrum of sentiments is vital.

Phase II: sentiment tagging

To apply the population game to the sentiment tagging task, we consider a review R as a game with two players: context and emotion. Each player has two strategies viz. positive ( p 1 ) and negative ( p 2 ). These strategies have values between 0 and 1, ensuring that their sum is always 1 ( p 1  +  p 2  = 1). We evaluated the numeric scores from Phase I are fed as input to Phase II. We follow Algorithm 3 to perform sentiment tagging of reviews using the population game model. In the algorithm, input is the payoff. The first step is the evaluation of the fitness of both players. The fitness of player 1 (context) is denoted as \({F}_{1}\left({\pi }_{1}, i\right)\) , and for player 2 (emotion), it is denoted as \({F}_{2}\left({\pi }_{2}, j\right)\) . These fitness measures capture the effectiveness of each strategy in the game. In the next step, the expected fitness of players 1 and 2 is \({E}_{1}\left({\pi }_{1}, {\pi }_{2}\right)\) and \({E}_{2}\left({\pi }_{1}, {\pi }_{2}\right)\) . The third step involves determining the replicator dynamics, which describes the rate of change of strategies over time. For player 1, this is represented by \(\dot{{p}_{i}}= \frac{d{p}_{i}}{dt}\) , and for player 2, it is represented by \(\dot{{q}_{j}}= \frac{d{q}_{j}}{dt}\) . As the time approaches infinity ( t  → ∞), the strategies of both players reach a point known as Nash equilibrium , where they no longer change their strategies. At this point, the rate of change of strategies for both players tends to zero i.e., \(\left( {\frac{{dp_{i} }}{dt} = 0\;{\text{and}}\;\frac{{dq_{j} }}{dt} = 0} \right)\) . The maximum value between p 1 , p 2 , q 1 , and q 2 indicates the sentiment tag assigned to review R . A detailed explanation of each step of Algorithm 3 is illustrated below from step 1 to step 6.

Sentiment tagging of reviews using population game

Step 1 : Define the Payoff matrix

The payoff matrix for the game is shown in Table 4 , which is the normal form representation of the game. In this study the choices made by the Context player and the Emotion player are not fully correlated; their strategies impact each other’s results. Here, the payoffs of player 1 (Context) are represented by the rows, and player 2 (emotion) are represented by the columns. Here, a ij represents the payoff received by player 1 (context) and player 2 (emotion). Similarly, b ij represents the payoff for player 2 under the same condition. For the proposed approach, the values in the table are derived from the context and emotion scores, as shown in matrix A and matrix B .

Table 4 shows that the strategies of the players are non-correlated, i.e., each player's choice is independent of the other player's choice. This means that the decision made by the context (Player 1) does not influence the decision made by the emotion (Player 2), and vice versa. In other words, we can say the strategy proportion of player 1 ( \({p}_{i}\) ) is independent of \(({q}_{j})\) . The outcome is determined by the combination of strategies chosen independently by each player.

Step 2 : Calculation of fitness of the players

The fitness of a player reflects its effectiveness in achieving desired outcomes within a given context. It measures how well the chosen strategy aligns with the goals of the situation. This evaluation considers the weighted combination of scores extracted from Phase I, encompassing factors like preferences, capabilities, and contextual constraints relevant to decision-making. Through this integration with chosen strategies, player fitness is assessed. This process determines how much each strategy contributes to achieving desirable outcomes based on the combined scores. Ultimately, these fitness values offer insights into the effectiveness of different strategies in meeting specific goals or requirements. The fitness of player 1 for the positive strategy is given by Eq. ( 11 ), and for the negative strategy is given by Eq. ( 12 ). The fitness of player 2 for the positive strategy is given by Eq. ( 13 ), and for negative is given by Eq. ( 14 ). Here, p i and q j are the proportions of individuals using strategy i and j respectively. The strategy proportion of player 1 is p 1 and p 2 and q 1 and q 2 is the strategy proportion of player 2.

For player 1 (Context)

For player 2 (Emotion)

Step 3 : Estimation of Expected Fitness

In this step, we evaluate the average expected fitness of players in the population by considering the proportions of different strategies. The expected fitness for player 1 ( E 1 ( π , π )) is given by Eq. ( 15 ), and for player 2 ( E 2 ( π , π )) is given by Eq. ( 16 ).

Step 4 : Evaluate Replicator dynamics

In this step, we use replicator dynamics to figure out the proportion of change in strategies over time. This is represented by Eqs. ( 17 ) and ( 18 ) for player 1. Equations ( 19 ) and ( 20 ) represent the replicator dynamics for player 2.

Step 5 : Steady state

After examining the rate of change of strategies over time, we analyze the population game's dynamics by observing the long-term behavior of replicator dynamics. This involves identifying a solution where strategies no longer change over time. This condition implies that as time progresses, a point comes when the rate at which strategies change slows down and eventually stops, i.e. \(\frac{d{p}_{i}}{dt}\) = 0 for player 1 and \(\frac{d{q}_{j}}{dt}\) = 0 for player 2. This point is the time when players cannot enhance their performances and are hence restricted to the same strategy. This stable state is called Nash equilibrium (NE). Equations ( 21 )–( 24 ) shows the conditions for a steady state for both the players.

Step 6 : Determine Sentiment Tag

In the population game model, the sentiment tag of reviews is determined by analyzing the proportion and stability of Nash equilibria. It is important to note that while Nash equilibria represent stable states, such that no player can improve their payoff by unilaterally changing their strategy, the dynamics of reaching such equilibria in replicator systems are highly dependent on the initial conditions. This dependency arises due to the nature of the differential equations governing the system, which can lead to convergence to different equilibria based on the starting point. The system's trajectory and the eventual equilibrium state are influenced by the initial values.

During our experimentation with various initial conditions, we observed that the results fluctuated and showed bias. To ensure no initial bias towards any strategy, we fix the initial conditions at p 1 (0) = 0.5, p 2 (0) = 0.5, q 1 (0) = 0.5, q 2 (0) = 0.5. This consistent starting point mitigates the influence of initial condition biases on the resulting sentiment tags. By fixing these neutral initial conditions, we achieve a more stable and repeatable process for determining the sentiment tag based on the dynamics of the population game model. By analyzing the most frequently occurring sentiment tags across player interactions and maintaining consistent initial conditions, we can reliably classify the sentiment of the text. We solve Eqs. ( 21 )–( 24 ) by using an analytic differential equation with the given initial conditions and evaluating the values of p 1 , p 2 , q 1 and q 2 . The ultimate tag to the text is given by Eq. ( 25 ).

In sentiment classification, context, and emotion tags are assigned based on the analysis of the text. Each sentiment tag's strategy proportion is denoted by p 1 , p 2 , q 1 , and q 2 for both players. To determine the most appropriate tag, we consider the maximum probabilities from both context and emotion analyses. Specifically, by taking the maximum of p 1 and p 2 , we select the tag with the highest confidence based on the context. Similarly, by taking the maximum of q 1 and q 2 , we select the tag with the highest confidence based on the emotion. When both the maximum confidence values from context (max ( p 1 , p 2 )) and emotion (max ( q 1 , q 2 )) converge to the same tag, it indicates that both analyses agree on the sentiment. This convergence is crucial as it provides higher reliability and confidence in the classification. The agreement of two independent assessments—context and emotion—reinforces the conclusion, making the sentiment classification more robust and dependable. In some cases, when context and emotion analyses disagree, it might be due to several factors such as sarcasm, mixed emotions, or complex contexts that are difficult to interpret accurately. Some of the cases are addressed in Section “ Recommendations for the future work ” as research limitations.

Numerical illustration

In this subsection, we took three illustrative examples of the above process being implemented on three reviews one with a different star rating.

Illustrative example 1

Let us consider an example review with a 4-star rating.

: ★★★★☆:

Initially, the context scores and emotion scores of the written review comment are computed as explained in Phase I using Algorithms 1 and 2, respectively. Table 5 contains the normalized context and emotion scores of the review.

Step 1 : Create Payoff Matrices

Table 6 is the normal form representation of the game, consisting of two players with two strategies along with their payoffs. With the help of Table 6 , we generate the two decision matrices— A and B as shown in Eqs. ( 26 ) and ( 27 ). Now we use these payoff matrices as input in Phase II for playing the population game model and sentiment tagging.

Step 2 : Calculate the Fitness of players. The fitness of player 1 ( F 1 ( π , i )) and player 2 ( F 2 ( π , j )) is expressed mathematically by Eqs. ( 28 )–( 31 ).

For Player 2 (Emotion)

Step 3 : Calculate Expected Fitness. The expected fitness of the players in the game, is given by Eqs. ( 32 ) and ( 33 ).

For Player 2 (emotion)

Step 4 : Replicator Dynamics Equation: We evaluate the replicator dynamics for player 1 and player 2 using Eqs. ( 34 )–( 37 ).

For player 1 (Context),

For player 2 (Emotion),

Using the constraints, p 1  +  p 2  = 1 and q 1  +  q 2  = 1 we get the Eqs. ( 38 )–( 41 ).

For Player 2 (emotion),

Using the analytic differential equation method with initial condition p 1 (0) = 0.5, p 2 (0) = 0.5, q 1 (0) = 0.5, q 2 (0) = 0.5, we get the values of p 1 ( t ), p 2 ( t ), q 1 ( t ), q 2 ( t ) as given below.

Step 5 : Determine Steady-State Value: For a further long time ( \(t\to \infty\) ) and at a steady state, the rate of change of strategies becomes zero with time. Given that both p 1 and q 1 initially start at 0.5, we observe the dynamic at ( \(t\to \infty\) ) for p 1 and q 1 increases over time towards 1, i.e., p 1  = 1, p 2  = 0 and q 1  = 1, q 2  = 0.

This ensures that the equilibrium solutions are consistent with the initial conditions and the dynamics defined by the replicator equations, providing a stable solution for the probabilities p 1 , p 2 , q 1 , and q 2 . The sentiment tag is determined by the maximum proportion of the strategies. Since both p 1 and q 1 represent positive strategies with higher values, the sentiment tag of the review is deduced as positive.

The plot of the replicator dynamics of the illustrative example is generated to see the sentiment dynamics. Figure  3 represents the plot generated from the replicator dynamics model. It illustrates the evolution of strategy proportions for both context and emotion over time. The x-axis represents the time span of the observation, while the y-axis shows the probability values of the strategies, ranging from 0 to 1.

Dynamic visualization of replicator dynamics.

The blue line represents that p 1 (Context Positive), starting at an initial probability of 0.5, rapidly increased to 1. This indicates the increasing proportion of the population adopting the positive context strategy, stabilizing at 1, which suggests the dominance of the positive context strategy. Conversely, the red line for p 2 (Context Negative) starts at 0.5 and quickly decreases to 0, indicating the extinction of the negative context strategy. Similarly, the green line represents q 1 (Emotion Positive), also starting at 0.5 and rapidly increasing to 1, showing the dominance of the positive emotion strategy. The yellow line for q 2 (Emotion Negative) starts at 0.5 and decreases to 0, indicating the extinction of the negative emotion strategy.

The rapid change observed, with positive strategies p 1 and q 1 quickly increasing and negative strategies p 2 and q 2 decreasing, demonstrates the system's tendency to Favor positive interactions. The system reaches a steady state where p 1 and q 1 are near 1, and p 2 and q 2 are near 0, indicating the overwhelming adoption of positive strategies. The dominance of p 1 and q 1 over p 2 and q 2 leads to the sentiment classification being "positive." This dynamic behavior aligns with the provided payoff matrices, where positive interactions are more beneficial for both context and emotion players. The rapid convergence to positive strategies suggests that the model strongly favors positive sentiment in the given setup.

Illustrative example 2

Let us consider an example review with a 2-star rating.

★★☆☆☆:

Initially, the context and emotion scores for the review comment are computed using Algorithms 1 and 2, as described in Phase I. These scores are detailed in Table 7 . In Phase II, we then apply the proposed methodology to these extracted scores to determine the sentiment tag. The evolution of the two strategies, based on different features, over time, is illustrated in Fig.  4 . Ultimately, we observe the convergence of these two strategies, which will determine the final sentiment tag of the text.

In the analysis conducted using Algorithm 3, the replicator dynamics plot (Fig.  4 ) demonstrates that as time progresses, the scores for context-negative and emotion-negative strategies converge towards 1, while the scores for context-positive and emotion-positive strategies approach 0. This trend indicates that the review sentiment classification is negative. This result shows that, despite any conflicting scores in the initial stages, the system stabilizes over time, consistently converging to an accurate negative classification for the review. Thus, the dynamics effectively resolve the sentiment classification to reflect the correct sentiment as negative, regardless of the initial discrepancies.

Illustrative example 3

Let us consider an example review with a 3-star rating.

★★★☆☆:

In accordance with the methodology described in Phase I, numeric scores are extracted, as presented in Table 8 . Subsequently, in Phase II, these scores are utilized to determine the sentiment tag, with the results illustrated in Fig.  5 .

Table 8 illustrates that, initially, the positive scores for both context and emotion features are dominated by the negative scores. After implementing the proposed methodology, we observe the following dynamics: At t  = 0, the game begins with initial strategies, and over time, the positive strategy for both context and emotion features converges to 1 as illustrated in Fig.  5 . Initially, the negative strategy for context increases but subsequently declines and converges to 0. Similarly, the negative strategy for emotion also converges to 0. This indicates that, ultimately, the positive strategy prevails over the negative strategy.

The population game model effectively captures these dynamics, demonstrating its robustness across different scenarios. The proposed methodology has proven effective in resolving conflicting scores and accurately tagging sentiment, as evidenced by the consistency and accuracy of the results across three different scenarios.

Human and animal ethics

No humans or animals were harmed in any way.

Result and evaluation

This section presents an overview of the datasets utilized for evaluating the performance of our population game model in both English and Hindi languages. We assessed our model's performance using several metrics, including accuracy, F1 score, and precision score. These metrics were compared against those achieved by state-of-the-art techniques to establish a benchmark for the effectiveness of our model. To ensure the validity of our comparisons, we adhered to the data annotation guidelines detailed in Section “ Annotation guidelines for model output verification ”. These guidelines ensured consistency in annotation across different methodologies. We evaluated the overall performance of our model by calculating macro and micro versions of accuracy, precision, F1 score, and recall. These metrics, presented in Sections “ Comparison of the proposed model with mathematical optimization techniques on the English dataset ” to “ Comparison of macro and micro evaluation ”, offer a comprehensive assessment of our model’s performance, reflecting both aggregated and individual performance metrics. Furthermore, we conducted a statistical analysis, as outlined in Section “ Statistical validation of the proposed model ”, to assess the significance of our proposed model. This analysis provided insights into the extent to which our model outperformed existing techniques, thereby highlighting its relative effectiveness.

The proposed algorithm was applied to six distinct datasets, covering both English and Hindi reviews. We used three datasets in English from diverse domains and three datasets in the Hindi language from varied domains. Each dataset contains written reviews paired with their respective ratings. Detailed data statistics are illustrated in Table 9 .

Annotation guidelines for model output verification

To evaluate the performance of the algorithm, the outcomes were compared with the sentiments annotated in the dataset. Manual annotation by domain experts was employed to classify the reviews based on sentiment. This manual annotation process involved the formation of an expert team with a strong understanding of sentiment analysis and the specific domain. Sentiment labels, positive and negative, were determined for the reviews. Annotation guidelines were created to compare the results of the population game algorithm with the rating scores specified in Eq. ( 42 ). Each written review was associated with a normalized rating score between 0 and 1, and based on this range, the reviews were labelled with positive or negative sentiment. Conflicts were resolved through expert discussions and collaboration within the team. Through this scientific approach, the manual annotation process by experts ensured reliable and accurate sentiment labelling for the reviews.

The purpose of this manual annotation was to evaluate and assess the accuracy of the population game model by comparing its results with the sentiments annotated in the dataset. This allowed us to measure the proposed model's performance, understand its strengths and weaknesses, and determine how well it aligned with the human-annotated sentiments. The comparison between the population game model's outputs and the annotated dataset served as a means of validating and verifying the model's accuracy by evaluating various metrics for performance measurement of the proposed population game model.

Why evaluating the neutral tag in reviews considered less critical?

While evaluating neutral sentiment can provide a more comprehensive understanding of sentiment distribution within textual data, in some contexts, it might be less critical or impactful for decision-making or analysis purposes compared to strongly polarized sentiments. Therefore, the importance of evaluating the neutral tag in reviews may vary based on the specific objectives, applications, and priorities of the sentiment analysis task at hand. Some of the main reasons we only consider the positive and negative tags are listed below:

Focus on polarized sentiments: Many sentiment analysis tasks prioritize distinguishing between positive and negative sentiments, as they often carry more significant implications for decision-making processes. Neutral sentiments, while valuable, might not influence decision-making or opinions to the same extent.

Application specificity: In certain applications, particularly those where decision-making relies heavily on polarized sentiments (e.g., customer feedback analysis, product reviews), assessing only positive or negative sentiments might hold more relevance than the neutral category.

Complexity and interpretation: Identifying neutral sentiment can be inherently more complex than identifying clear positive or negative sentiments. The ambiguity or subjectivity in what constitutes neutral sentiment makes its evaluation more challenging.

Resource allocation: Resources, including time and computational power, might be better allocated towards distinguishing and analyzing polarized sentiments, which often have a more direct impact on decision-making.

Evaluation of the population game model’s performance

Evaluating performance metrics for frameworks is crucial as it provides a systematic and quantitative approach to assess the performance, reliability, and effectiveness of the framework. These metrics offer objective measures to gauge how well the framework achieves its intended goals, whether it's classification accuracy, anomaly detection, or any other task. By evaluating these metrics, researchers and practitioners can pinpoint the strengths, weaknesses, and areas for improvement within the framework. Additionally, it enables comparison with alternative frameworks or approaches, facilitating the selection of the most suitable tool for a given problem domain. The use of various metrics such as accuracy, F1-score, recall, precision, and MCC score is fundamental in assessing models as they provide diverse insights into performance. Accuracy reflects overall correctness by measuring the proportion of correctly classified instances. Precision underscores the accuracy of positive predictions, crucial when minimizing false positives is paramount. Recall focuses on capturing all relevant instances, especially vital in scenarios where missing positives incur significant costs. F1-score, harmonizing precision and recall, delivers a balanced assessment, particularly valuable in dealing with imbalanced datasets. Together, these metrics offer a comprehensive understanding of a model's efficiency, facilitating model selection, parameter tuning, and optimization, ultimately elevating the reliability and effectiveness of machine learning systems. These metrics are evaluated using Eqs. ( 43 ), ( 44 ) which employs TP, TN, FP, and FN as essential indicators for assessing classification models. True positives (TP) refer to cases that are correctly identified as positive, whereas true negatives (TN) are instances that are accurately classified as negative. False positives (FP) arise when the model erroneously classifies an instance as positive when it should be negative, while false negatives (FN) occur when the model mistakenly classifies an instance as negative when it should be positive. TP and TN denote accurate forecasts, but FP and FN signify prediction inaccuracies. These measures are crucial for evaluating the model's accuracy, precision, recall, and other performance indicators, offering vital insights into its capacity to effectively categorize cases. In the appendix subsection, we assessed the performance of the population game model across several datasets illustrated in Tables 9 , 10 , and 11 .

Comparison of the proposed model with mathematical optimization techniques on the English dataset

In this section, we conduct a comparative analysis between the proposed model and several established sentiment analysis approaches, including SA-MpMcDM 22 , COPGT-ST 28 , NEGVOT 29 , SOTM 5 , GRA-ST 24 , BGM-ST 23 , and TOGT-ST 3 . Among these, six models are rooted in Multiple Criteria Decision Making (MCDM) techniques, while BGM-ST is based on the Bayesian game model. The performance of these models is depicted through the evaluation metrics of accuracy (refer Fig.  6 ), precision (refer Fig.  7 ), and F1-score (refer Fig.  8 ). The numeric scores of Eqs. ( 26 )–( 28 ) metrics are in Table 1 of the appendix section. SA-MpMcDM, an MCDM-based model, achieved accuracies of 0.71 on the IMDB movie reviews dataset, and 0.68 and 0.67 on the Electronic and Trip-Advisor datasets, respectively. However, the dependency of SA-MpMcDM on the selection and weighting criteria may lead to varying performance across different datasets, making it less robust in diverse settings. COPGT-ST, integrating the COPRAS MCDM technique with a non-cooperative game model, attained accuracies of approximately 0.79 on IMDB movies, 0.78 on electronic reviews, and 0.76 on TripAdvisor reviews. While commendable, the performance of COPGT-ST may suffer from scalability issues and the need for fine-tuning parameters to achieve optimal results. NEGVOT, utilizing the VIKOR MCDM technique, scored around 0.79 on IMDB movies, 0.8 on electronic reviews, and 0.79 on TripAdvisor reviews datasets. Despite its competitive accuracy, NEGVOT's reliance on specific optimization strategies may limit its generalizability and adaptability to different domains.SOTM, employing the Simple Additive Weighting (SAW) technique with a non-cooperative game model, achieved the highest accuracy of 0.85 on the electronic dataset. However, SOTM's performance may be sensitive to the choice of weighting scheme and may struggle with capturing complex relationships in the data. GRA-ST, employing the Grey Relational Analysis (GRA) MCDM technique, achieved the highest accuracy of 0.81 on the IMDB movie dataset. While effective, GRA-ST may face challenges in handling high-dimensional data and may not scale well to larger datasets. Similarly, BGM-ST, a Bayesian game model-based sentiment tagger incorporating context and rating score, reached the highest accuracy of around 0.81 on the TripAdvisor dataset. However, BGM-ST's reliance on additional contextual information such as ratings alongside text may limit its applicability in scenarios where such information is unavailable or irrelevant. TOGT-ST, an integrated MCDM and game theory-based model assigning sentiment tags to text, achieved an accuracy of 0.82 on the TripAdvisor review dataset. Nonetheless, TOGT-ST's performance may be affected by the complexity of the decision-making process and the need for domain-specific adjustments. In contrast, the proposed model consistently outperforms existing approaches across all evaluations. The comparatively lower accuracies of MCDM-based models stem from their dependency on the number and weighting of criteria used. Additionally, BGM-ST's dependency on the presence of ratings alongside text restricts its domain of implementation. In contrast, the proposed model overcomes these limitations. It does not rely on weights and maintains consistent sentiment tags over time, making it a robust framework for sentiment analysis. Its decentralized decision-making process harnesses collective intelligence, mitigating individual biases and offering superior performance in diverse data settings. With its scalable and adaptable nature, the proposed model emerges as a reliable solution for sentiment analysis tasks, achieving the highest accuracy of 0.85 on the trip-advisor reviews dataset out of three in the English language.

Comparison of the proposed model with optimization techniques-based sentiment taggers in terms of accuracy.

Comparison of the proposed model with optimization techniques-based sentiment taggers in terms of precision.

Comparison of the proposed model with optimization techniques-based sentiment taggers in terms of F1-measure.

Comparison of the proposed model with existing approaches in English Text

In this subsection, we conduct a comparison between the proposed model and several existing approaches in terms of accuracy. These approaches include W2VLDA, LSVM classifier, SDA model, B-MLCNN, LDA, Apriori model, and DOC-ABSADeepL model. We applied these models to three datasets from different domains and evaluated their accuracy for comparative analysis, as illustrated in Fig.  9 . Numeric scores of Eqs. ( 28 )–( 30 ) metrics are tabulated in Table 2 of the appendix section.

Comparison of the performance of the proposed model with existing approaches in terms of accuracy.

Atandoh et al. 21 introduced the BERT-MultiLayer Convolutional Neural Network (B-MLCNN) as an integrated deep learning paradigm for sentiment analysis. Despite achieving a commendable accuracy of approximately 0.81 for IMDB movie reviews, B-MLCNN's reliance on treating entire textual reviews as a single document limits its ability to capture nuanced sentiments within longer texts. Furthermore, while B-MLCNN outperforms in certain domains, its performance diminishes in others, highlighting its lack of domain adaptability. Additionally, the proposed model exhibited better performance as compared to Buon Appetito 30 . Apriori on IMBD and Trip-Advisor its accuracy is 0.79, and on the electronic dataset, it is recorded as 0.8. The Latent Dirichlet Allocation (LDA) 31 based model achieved respectable accuracies, including 0.78 on IMDB movie reviews and 0.85 and 0.83 on electronic and Trip-Advisor reviews, respectively. However, LDA's performance may suffer from its inherent reliance on topic modeling, which may not fully capture sentiment nuances. In contrast, the proposed model offers a more holistic approach to sentiment analysis, overcoming the limitations of topic-based models by directly analyzing sentiment expressions, thereby ensuring more accurate sentiment classification across various datasets. Daniel and Meena’s 12 LSVM classifier approach achieved accuracies of around 0.71 and 0.68 on IMDB and Amazon electronic review datasets, respectively. However, LSVM's performance may be limited by its dependency on specific feature representations, which may not fully capture the complexity of natural language sentiment. In contrast, the proposed model does not rely on predefined feature representations, allowing it to adapt more effectively to different datasets and capture subtle sentiment nuances more accurately. Similarly, W2VLDA by Garcia-Pablos et al. 7 achieved accuracies of 0.8 for the electronic dataset, 0.79 for IMDB, and 0.81 for the TripAdvisor dataset. While W2VLDA combined word embeddings with LDA for sentiment analysis, it may still face challenges in accurately capturing sentiment nuances, especially in complex language expressions. In contrast, the proposed model leverages population dynamics to capture the collective sentiment of a group, thereby providing a more robust and accurate sentiment analysis across various linguistic contexts.

Selective Domain Adaptation (SDA) 15 focused on selectively transferring knowledge from source to target domains at the feature level and achieved its highest accuracy of 0.81 for the TripAdvisor dataset. However, SDA's performance may be hindered by its reliance on specific domain features, which may not generalize well to other domains. In contrast, the proposed model does not rely on domain-specific features, allowing it to perform consistently well across diverse datasets without the need for domain-specific adaptations. DOC-ABSADeepL 22 methodology incorporated expert evaluations based on natural language reviews and numerical ratings. However, the proposed model demonstrates superior precision, recall, and f1-measure when compared to DOC-ABSADeepL. DOC-ABSADeepL's reliance on expert evaluations may limit its scalability and adaptability to different datasets and linguistic contexts. In contrast, the proposed model's data-driven approach ensures robust and accurate sentiment analysis across diverse datasets and linguistic expressions achieving an accuracy of 0.86.

Comparison of the proposed model with mathematical optimization techniques on Hindi text

In this section, we compare the performance of the proposed model on the Hindi dataset against several mathematical optimization models, including TOGT-ST 3 , BGM-ST 23 , GRA-ST 24 , SOTM 5 , NEGVOT 29 , and COPDT-ST 28 . Numeric scores of Eqs. ( 28 )–( 30 ) metrics are tabulated in Table 3 of the appendix section. To evaluate the performance of the proposed model on Hindi reviews, we tweaked the proposed approach a bit. To fetch the context scores of the Hindi review comments, instead of SWN, we employed Hindi SentiWordnet (HSWN). HSWN is a collection of Hindi words and their associated positive and negative sentiment values. Once, we had the context scores of Hindi reviews, the rest of the approach remained the same.

COPDT-ST, formulated by integrating the COPRAS MCDM technique with a non-cooperative game model, achieved an accuracy of approximately 0.74 for electronic reviews and around 0.76 for movie reviews. While achieving commendable accuracy, COPDT-ST may suffer from computational complexity and scalability issues due to its integration of multiple decision-making techniques. NEGVOT demonstrates the highest accuracy of approximately 0.7 for movie reviews. However, it may struggle with generalizability and robustness across different domains due to its reliance on specific optimization strategies tailored for movie review datasets. SOTM, an amalgamation of the SAW technique and other non-cooperative game models, achieved accuracies of 0.66 for electronics, 0.69 for hotels, and 0.689 for movies. Despite its versatility, SOTM may lack adaptability to dynamic data environments and could be sensitive to parameter settings. GRA-ST, utilizing the GRA, MCDM technique, achieves its peak accuracy at 0.76. While GRA-ST offers a robust methodology for decision-making, it may face challenges in handling complex decision landscapes and may be less effective in scenarios with high-dimensional data. Similarly, BGM-ST, grounded on the Bayesian game model, achieves an accuracy of around 0.66 on electronic review datasets. Bayesian game models, while theoretically sound, may require strong prior knowledge and assumptions about the underlying data distribution, limiting their applicability in real-world scenarios. TOGT-ST, an integration of the TOPSIS MCDM technique and a non-cooperative game model achieves the highest accuracy in hotel domain reviews at around 0.77. However, its performance in other domains may vary, indicating potential domain-specific biases or limitations in generalizability. Evaluation metric measures for all these models consistently reveal the superior performance of the proposed model when compared to other models, as shown in Figs. 10 , 11 , and 12 . The proposed model not only demonstrates competitive accuracy but also offers advantages in terms of scalability, adaptability, and robustness across diverse datasets and archived accuracy of 0.88 on movie reviews dataset. By leveraging collective intelligence and decentralized decision-making, it mitigates individual biases and outperforms traditional optimization models in real-world applications.

Comparison of accuracy of the population game model with optimization techniques across various domains.

Comparison of Precision the population game model with optimization techniques across various domains.

Comparison of F1-score the population game model with optimization techniques across various domains.

Comparison of macro and micro evaluation

In this section, we conduct a comparative analysis between the macro and micro-level scores of the Bayesian game model and the proposed model. The examination reveals the superior performance of the proposed model in terms of accuracy, precision, and recall metrics. With a macro F1-score of 0.88 for the proposed model and micro precision is 0.89 across the four datasets, the former demonstrates enhanced reliability. Figure  13 presents an overview of the performance across six datasets, reinforcing the superiority of the proposed model.

Performance of population game model over Bayesian game model.

Statistical validation of the proposed model

Two distinct samples were extracted from datasets containing hotel reviews and movie reviews, respectively. In the first sample, a total of 1000 reviews ( n 1 ) were gathered and subsequently analyzed. Of these reviews, 910 were accurately classified, yielding a proportion of 0.91 ( p 1 ). Likewise, the sample size ( n 2 ) in the second group was 252, and out of a total of 500 reviews, 410 were accurately categorized, resulting in a sample proportion of 0.896 ( p 2 ). A Z-test was conducted to analyze the proportions of two populations ( p 1 and p 2 ). The data statistics are presented in Table 10 .

To perform the z-test, we define a null hypothesis (H 0 ) and an alternate hypothesis ( H a ) as given below.

p 1 = p 2 i.e., the accuracy of sample 1 is equal to the accuracy of sample 2

p 1 ≠ p 2 i.e., the accuracy of sample 1 is not equal to the accuracy of sample 2

Two population proportions were analyzed using a two-tailed test and a z-test as shown in Fig.  14 . The z-statistic is computed utilizing Eq. ( 45 ).

An illustration of the hypothesis’s critical zone.

The critical region of the assumed hypothesis is depicted in graphical form in Fig.  14 . Based on the statistical analysis results, the null hypothesis (H o ) was not rejected, indicating a lack of sufficient evidence to support the claim that the population proportions p 1 and p 2 are different at the 0.05 significance level. Hence, it can be concluded that there is insufficient statistical support to assert a significant disparity between the two proportions. This suggests that the percentage of correctly classified reviews remains consistent across datasets with different sample sizes. As a result, it can be inferred that the proposed model consistently produces reliable outcomes.

In this section, we discuss the proposed model’s error rate, significance, practical applications, limitations, and areas for improvement.

Error analysis

The error rate in model evaluation is a fundamental metric used to gauge the performance of machine learning models. It represents the proportion of incorrect predictions made by a model on a given dataset. This metric holds significant importance as it quantifies the model's ability to generalize to unseen data and its overall predictive accuracy. Figure  15 illustrates a comparison of error rates between the proposed model and existing models. Notably, the LSVM classifier and SA-MpMcDM model exhibit the highest error rates, approximately 0.29, while the proposed model demonstrates the lowest, at 0.16. The remaining models fall within the error rate spectrum of 0.29–0.16. This analysis underscores the effectiveness of the proposed model in minimizing prediction errors compared to its counterparts.

Error rate comparison across different models.

Significance of the proposed model

This study proposes a methodology for conducting sentiment analysis by utilizing a mathematical optimization framework. The proposed model's unsupervised nature is a significant advantage. No extensive dataset annotation is necessary. The proposed model utilizes data that are unlabelled and unclassified. The sole prerequisite is the availability of SWN or analogous lexicon resources that can be utilized for the computation of context scores of reviews. The model is efficient in both space and time. The study paves the way for the implementation of mathematical optimizations in diverse NLP applications.

Real-time insights: utilizing dynamic models for effective sentiment analysis

The proposed framework applies to real-time textual data as it relies on two key inputs: context and emotion scores. These scores can be generated using Algorithms 1 and 2, making the framework adaptable to various textual data domains. The four scores extracted in Phase I serve as inputs for Phase II, allowing implementation across different domains. Potential real-time domains for implementing this framework include:

Market Intelligence and Brand Management:

The model analyzes textual data such as social media posts, comments, reviews, and feedback to understand consumer sentiments towards brands, products, or services. It identifies strategic interactions among consumers, modeling how sentiments evolve and spread within the population. By considering the dynamics of sentiment propagation as strategic choices made by individuals, the model provides more accurate predictions of market trends and brand perceptions. It also identifies key influencers whose sentiments significantly impact overall trends and perceptions, enabling targeted marketing strategies.

Social Media Monitoring and Trend Analysis:

For social media data, the model captures the strategic interactions among users through their textual engagements. It analyzes how sentiments are expressed, shared, and influenced within online communities, identifying influential individuals or groups driving trends. By modeling sentiment spread as strategic interactions, the model can anticipate emerging trends, crises, or opportunities in real time. It provides insights into sentiment dynamics and helps organizations respond effectively to changing public opinion on social media platforms.

Political Analysis and Public Opinion Research:

In the political domain, the model examines textual data such as social media discussions, news articles, and polling data to understand voter sentiments and behavior. It models political sentiments as strategic choices made by individuals within the population, considering factors like candidate preferences and policy perceptions. By analyzing these interactions, the model predicts electoral outcomes more accurately and helps policymakers gauge public sentiment toward policies and initiatives. It enables evidence-based decision-making and responsive governance by understanding the strategic dynamics of sentiment within the population.

Customer Feedback Analysis and Service Improvement:

For customer feedback data, the model analyzes textual inputs from various channels like surveys, reviews, and social media comments. It identifies patterns, trends, and outliers in customer sentiments, considering factors like product experiences and service interactions. By modeling customer sentiments as strategic choices, the model provides actionable insights for service improvement strategies. It helps businesses understand the underlying dynamics of customer sentiment within the population, enabling targeted interventions and personalized approaches to address customer needs effectively.

Overall, the population game model excels in sentiment analysis by capturing the strategic interactions among individuals based on textual data, providing deeper insights into market trends, public opinion, and customer preferences across diverse domains.

Research limitations

In this subsection, we examine the error detection in one case where the proposed methodology fails.

Case 1 : Ambiguous Sentiment Tags: A significant limitation of the proposed model is when p 1  =  p 2  =  q 1  =  q 2  = 0.5. This situation indicates an equal probability for both positive and negative sentiments, leading to ambiguity illustrated in example 2.

: ★★★☆☆

In Phase I, we initially extract the numeric scores as demonstrated in Table 11 . Subsequently, these numeric scores are input into Phase II, where sentiment tags are assigned to the text. The final output is illustrated in Fig.  16 . Output illustrated by the replicator dynamics plot shown in Fig.  16 , the results indicate that the positive context score converges to 0, while the negative context score converges to 1. Meanwhile, both the positive and negative emotion scores stabilize at an initial condition value of 0.5. This pattern reveals significant conflicting results among the four parameters. The dynamics exhibit a clear shift where context strategies reach a definitive state, but the emotion strategies remain unresolved at their initial levels, highlighting inconsistencies in sentiment classification. In this case, both the context and emotion analyses result from different strategies. This indicates an ambiguous sentiment where neither positive nor negative sentiment is dominant. This is also a unique scenario where there is no clear dominant sentiment, and both are equally probable. In this case, we get the absurd and incorrect answers. This might require additional rules or more sophisticated handling to determine the final tag or to recognize it as an inherently ambiguous sentiment.

Replicator dynamic plot of the review.

Recommendations for the future work

The future of this research holds promising advancements driven by technological innovations and evolving needs in various industries. Some potential directions for future work in sentiment analysis include:

Fine-grained sentiment analysis: Current sentiment analysis often categorizes text into positive, negative, or neutral sentiments. Future work aims to enhance this by detecting and analyzing more nuanced emotions, opinions, and intentions within the text, such as sarcasm, irony, humor, or subtle sentiments.

Multimodal sentiment analysis: We may aim to integrate multiple data modalities like text, images, videos, and audio for sentiment analysis. This involves understanding sentiment expressed not just through text but also through visual and auditory cues, enhancing the accuracy and depth of sentiment interpretation.

Contextual understanding: Improving algorithms to better understand the context in which sentiment is expressed. Considering the broader context of conversation, cultural nuances, user-specific contexts, and historical data can help in more accurate sentiment analysis.

Aspect-based sentiment analysis: The target will be to focus on identifying sentiments towards specific aspects or entities within the text. For instance, in a product review, understanding sentiments towards different features of the product rather than general sentiment about the whole product.

Overall, the future of sentiment analysis is likely to focus on more precise, context-aware, and adaptable models that can understand human sentiments more accurately across various mediums and contexts while addressing ethical considerations and user-specific needs.

In this study, we employed a population game model specifically designed for sentence-level sentiment analysis. We conducted a rigorous evaluation across diverse datasets in both English and Hindi, covering various domains such as movie reviews, hotel reviews, electronic device reviews, and IMDB movie ratings. Our findings demonstrate the model's language and domain independence. Utilizing a range of evaluation metrics, we achieved a maximum macro accuracy of 89%. We thoroughly examined error cases, and limiting scenarios, and discussed real-life implementation considerations, alongside offering recommendations for future program enhancements. Furthermore, our framework incorporates statistical cross-validation techniques, including Z-tests, to assess its performance robustly. The results reveal the promising performance of our model and affirm its competency in sentiment classification tasks. In our forthcoming research, we aim to delve deeper into sentiment analysis nuances, such as aspect-based sentiment analysis, and address challenges such as negation handling, irony detection, and multimodal framework generation. Our future endeavors will focus on utilizing advanced mathematical and statistical frameworks by integrating additional textual features and employing fuzzy-based approaches to effectively address various challenges of sentiment classification within the text.

Data availability

The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.

Code availability

The code generated during the current study is available from the corresponding author upon reasonable request.

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Research Article

Prediction of infectious diseases using sentiment analysis on social media data

Roles Conceptualization, Data curation, Formal analysis, Methodology, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation Department of Industrial & Systems Engineering, Dongguk University, Jung-gu, Seoul, South Korea

Roles Conceptualization, Formal analysis, Funding acquisition, Project administration, Supervision, Validation, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

• Youngchul Song, 
• Byungun Yoon

• Published: September 4, 2024
• https://doi.org/10.1371/journal.pone.0309842
• Reader Comments

As the influence and risk of infectious diseases increase, efforts are being made to predict the number of confirmed infectious disease patients, but research involving the qualitative opinions of social media users is scarce. However, social data can change the psychology and behaviors of crowds through information dissemination, which can affect the spread of infectious diseases. Existing studies have used the number of confirmed cases and spatial data to predict the number of confirmed cases of infectious diseases. However, studies using opinions from social data that affect changes in human behavior in relation to the spread of infectious diseases are inadequate. Therefore, herein, we propose a new approach for sentiment analysis of social data by using opinion mining and to predict the number of confirmed cases of infectious diseases by using machine learning techniques. To build a sentiment dictionary specialized for predicting infectious diseases, we used Word2Vec to expand the existing sentiment dictionary and calculate the daily sentiment polarity by dividing it into positive and negative polarities from collected social data. Thereafter, we developed an algorithm to predict the number of confirmed infectious patients by using both positive and negative polarities with DNN, LSTM and GRU. The method proposed herein showed that the prediction results of the number of confirmed cases obtained using opinion mining were 1.12% and 3% better than those obtained without using opinion mining in LSTM and GRU model, and it is expected that social data will be used from a qualitative perspective for predicting the number of confirmed cases of infectious diseases.

Citation: Song Y, Yoon B (2024) Prediction of infectious diseases using sentiment analysis on social media data. PLoS ONE 19(9): e0309842. https://doi.org/10.1371/journal.pone.0309842

Editor: Shady Elbassuoni, American University of Beirut, LEBANON

Received: June 24, 2023; Accepted: August 20, 2024; Published: September 4, 2024

Copyright: © 2024 Song, Yoon. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: This work was supported by the National Research Foundation of Korea under Grant NRF-2021R1I1A2045721 and the funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Infectious diseases are diseases that can spread from person to person and have continued to occur throughout human history. Since the first epidemic was recorded around 430 B.C., many infectious diseases have had huge impacts on mankind, such as the Black Death, smallpox, Spanish flu, and cholera. The Black Death killed approximately a third of Europe’s population, and smallpox has killed more than a billion people thus far. These disease epidemics have had major impacts on the overall economic conditions of the countries in which they occurred. COVID-19, which started in December 2019, has influenced many countries and has changed the lives of modern humankind. The World Health Organization (WHO) declared COVID-19 a pandemic, which is the highest risk level for infectious diseases, in March 2020. The declaration served as a starting point for the establishment of quarantine systems in each country in recognition of the severity of the pandemic. As human and property damage due to the COVID-19 pandemic increase [ 1 ], the pandemic can be classified as a social disaster that has caused large-scale damage at the national level. To date, the need to present health strategies for predicting infectious diseases and minimizing damage has emerged in the world, such as the implementation of distance-by-step and COVID-19 support policies.

With the increasing risk and impact of infectious diseases, researchers are uncovering the necessary data and methods to accurately forecast the number of confirmed cases. From a data perspective, most studies have employed daily confirmed case data to make predictions using regression or machine learning (ML) techniques [ 2 – 4 ]. In addition, some studies have been carried out to forecast the number of confirmed cases by identifying additional elements that influence the transmission of infectious illnesses, such as spatial data [ 5 , 6 ]. However, there is a notable deficiency in integrating the subjective parts of social data, such as sentiment analysis, into models used for predicting infectious diseases. Thus, our study anticipates that including social data with these parameters will yield advantages.

This study begins with the assumption that the spread of infectious diseases is related to the sentiment polarity of social media. If a lot of negative sentiments are posted on social media, people will act more carefully, reducing the spread of the epidemic, and if the word "it’s okay" comes out a lot, people will be able to act casually and speed up the spread of the epidemic. When information pertaining to the risk of the coronavirus is spread through social networks, negative events can be transmitted through repeated exposure, resulting in acute stress [ 7 ]. The stress of this infectious disease causes people to change their behaviors to cope with it [ 8 ]. Since the start of COVID-19, people using social media data have been used to understand public psychological responses related to infectious diseases. In a survey, 93.3% of respondents stated that they avoid going to public places, 89.6% of the respondents reduced holiday-related activities, and more than 70% of the respondents stated said they take precautions to avoid infection [ 9 ]. Changes in people’s behaviors and the implementation of preventive measures in infected areas can affect the population density and quarantine, thereby curbing the spread of infectious diseases [ 10 – 12 ]. Therefore, it is considered meaningful to predict the number of confirmed infectious disease cases by analyzing people’s opinions pertaining to infectious diseases on social networks. This study aims to predict the number of confirmed cases of infectious diseases by using anonymized social media data containing collective public opinions on infectious diseases.

Considering this perspective, search volumes were used to predict the number of confirmed cases [ 13 ]. Sentiment analysis was conducted to explore the qualitative aspect of social data, and in [ 14 ], the number of future vaccinations was predicted on the basis of an setiment analysis of tweet data. To predict the number of confirmed infectious disease patients, daily numbers of confirmed cases and quantitative approaches to social and public data are being used. However, the above-referenced studies reflecting the qualitative characteristics of social data, which affect people’s psychology in terms of the number of confirmed infectious disease patients, are insufficient. Therefore, this study analyzes the qualitative characteristics of social data by means of opinion mining to check whether there exists a relationship between people’s sentiment states and prediction of the number of confirmed cases.

The motivation for this study lies in the observation that the social networking behavior of individuals can have an impact on the transmission of infectious diseases. Therefore, it is important to take this factor into account when forecasting the number of confirmed cases. This study utilizes data from social network services (SNS) to examine how the public responds to information about infectious diseases. It uses sentiment analysis, a method within the field of opinion mining, to analyze the sentiment expressed in these answers. The sentiment data that is retrieved is subsequently employed to forecast the quantity of confirmed cases of infectious diseases by utilizing machine learning models, with the objective of evaluating the accuracy of the predictions. The key findings of this study indicate that incorporating social media sentiment data into infectious disease prediction models results in better predictive performance compared to models that do not consider such data. This underscores the potential significance of social media data in improving the accuracy of infectious disease predictions. The study is structured as follows. Background explains the background theory of the contents covered in this study. Research Framework explains the research framework. The methods used herein are described in Results, and the results obtained using these methods are presented in Implications & Discussion. Finally, Conclusion presents the limitations and future directions of this research.

In this section, we review the extant literature on epidemic prediction, latest opinion mining processes, and ML models used for time-series prediction. First, we review how studies on infectious disease prediction have been conducted thus far, ML techniques used herein to predict the number of confirmed cases, and methods for opinion mining of social data.

Predicting infectious diseases

To predict infectious diseases, Kemack and McKendrick proposed an infectious disease spread model by devising an SIR (Susceptible, Infectious, Recovered) model that considers uninfected, infected, and recovered people [ 15 ]. Assuming that all populations have the above population configuration, a series of differential equations were used to indicate the state of the overall population in terms of the number of infections. In this model, the formula was completed using the infection rate and recovery rate for each infectious disease, and studies on infectious diseases are still being conducted by using the SIR model and the modified SEIR (Susceptible, Exposed, Infectious, Recovered) model [ 16 – 18 ].

Moreover, in recent studies, with the advancement of artificial intelligence (AI), the number of confirmed infectious disease patients has been predicted using the ML and deep learning (DL) approaches, which are unlike the conventional model. The AI-based approaches consider diverse variables that affect infection, rather than merely considering the infection rate and recovery rate, which represent the unique characteristics of existing infectious diseases. This improves the prediction ability in dynamic situations. The number of confirmed cases in the early stages of COVID-19 was predicted using the ARIMA and TP-SMM-AR self-regression time-series models, respectively [ 19 ]. The Holt’s time series model was also used for forecasting confirmed cases, relying solely on global confirmed case data to predict future cases [ 4 ]. The ARIMA, Holt, Splines, and TBATS models were also used to predict confirmed cases, deaths, and cured cases of And USA and Italy [ 20 ]. In another study, simulations were conducted to create confirmed scenarios, and the impact and transmission order of spread were studied [ 5 ]. In studies using ML and DL, DNN, LSTM and gated recurrent unit (GRU) were used to predict the number of confirmed infectious disease patients [ 2 , 6 , 18 , 21 – 24 ]. In addition, several ML techniques (K-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF)) have been used to predict the number of people vaccinated [ 14 ]. The study exploited past pandemic case data to create a nonlinear autoregressive neural network time series model for forecasting confirmed cases. The studies primarily focused on making time series forecasts using solely confirmed case data, but also using other forms of data such as spatial data. While several studies have made predictions about the number of confirmed cases based on social data, they mostly relied on quantitative indicators obtained from social networks [ 13 ]. The models and data used in the previous studies are shown in Table 1 . Some of these studies argue that social information can be analyzed for predicting confirmed infectious disease patients.

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https://doi.org/10.1371/journal.pone.0309842.t001

The best tools and data for predicting a dynamic epidemic such as COVID-19 are not specified. The data and tools that can be used to predict infectious diseases continue to be discovered to date. From the data perspective, a model that employs the results of opinion mining of social data can be tried.

Opinion mining

Opinion mining is a big data analysis technique for analyzing and processing vast amounts of social text data. At the system level, it calculates the sentiment polarity of text sentences and is also called sentiment analysis. Many people read other people’s writings, and their behaviors are influenced by these writings, which can be analyzed through sentiment analysis [ 25 , 26 ]. Sentiment analysis yielded significantly superior results on opinion-classification tasks than those of other text mining approaches [ 27 ]. Opinion mining can be used to identify people’s behavioral characteristics and expected phenomena through trend analysis and future prediction by using large numbers of opinions published on the Internet. The opinion mining of text data related to a specific topic facilitates the development of interesting approaches to the topic. An example is Obama’s successful 2012 election campaign, in which opinion mining was used, and analyses of buyers and users’ reviews by using opinion mining to gain insights in many customer analysis studies [ 28 – 30 ].

Usually, the process of opinion mining is as follows. First, the study targets are identified, and data with characteristics that the targets write or represent the target is collected and preprocessed. Thereafter, attributes such as opinions and attitudes, degrees of positivity/negativity, and satisfaction are used to select the characteristics to be extracted from the data. In the sentiment analysis conducted herein, positive/negative values are extracted, and to extract polarities, sentiment dictionaries and rule-based polarities are typically derived. Sentiment dictionary can analyze text data by using the words, rules, and polarities predefined in the sentiment dictionary to calculate positive/negative values depending on keyword appearance or rules [ 29 – 31 ]. Recently, a method of sentiment classification using ML and DL was studied [ 31 ].

Studies on the sentiment dictionaries used in sentiment analysis are being conducted. Because sentiment dictionaries use predefined values, it is important to build a sentiment dictionary that tailored to the corpus being analyzed. In previous studies, sentiment dictionaries were expanded successfully by using Word2Vec. Word2Vec is a word embedding technique that was introduced in 2013, and it uses a continuous bag of words (CBOW) learning method that predicts one blank by using multiple inputs and a skip-gram learning method that predicts surrounding blanks by using one input. The words learned in this manner have their respective vector values. In previous studies, the existing sentiment dictionaries were expanded using the cosine similarity of the Word2Vec results, and word dictionaries that were better optimized for the dataset to be analyzed were established [ 32 – 34 ]. In this study, sentiment analysis of social data is conducted by producing an extended sentiment dictionary by using Word2Vec in line with the changing characteristics of the existing sentiment dictionaries and social data.

Machine learning

ML is being used in many predictive studies. ML is mainly divided into guidance, semi-supervised, and unsupervised learning depending on the learning method. Although ML is a black box model, meaning that how the model arrives at its results is not known, it is generally used in many fields such as recognition, classification, and prediction. Moreover, many predictive studies are underway to demonstrate strengths in the field of time-series prediction, and RNN techniques specialized for time-series analysis by remembering existing data are available. In addition, LSTM and GRU techniques have been derived from RNN. These models continue to be used for predicting infectious diseases. The present study aims to predict the number of confirmed infectious disease patients by using a deep neural network (DNN), a basic machine learning technique, in conjunction with LSTM and GRU specialized for time-series analysis.

A DNN is an artificial neural network that calculates outputs by multiplying weights across multiple hidden layers [ 35 ]. The DNN structure, illustrated in Fig 1 , consists of an input layer, a hidden layer, and an output layer. These layers are connected to each other, and values are transformed and moved by using weights and activation functions. Each weight is modified by learning, and the network is created using the modified weights. DNNs are mainly used in supervised learning to solve classification and regression problems. When the predetermined learning process is completed, the result value of the new input value is derived using the final calculated weight. This DNN structure is also used for various tasks by connecting it to other ML techniques.

https://doi.org/10.1371/journal.pone.0309842.g001

LSTM is a circular neural network technique that was developed to overcome the limitations of RNN, which exhibits reduced learning ability owing to weak influence of past information [ 36 ]. The structure of LSTM is depicted in Fig 2 , and LSTM learns by controlling the memory or by forgetting past information. In the figure, the flow of Ct refers to the cell state of the previous data; new information and previous ht are used to decide whether to preserve or discard information; input gate is added and multiplied using the sigmoid and tanh functions; and, finally, cell state is updated. In the output gate, ht is calculated using the sigmoid and tanh functions, which represents the short-term memory status and is identical to the value calculated in the corresponding cell and flowing out to the output. In conclusion, the result value is learned and derived using long-term memory, short-term memory, and new input information. LSTM with these characteristics is widely used for time-series analysis, and specifically, it is useful for time-series analysis involving volatility. The LSTM model has also been used from a time-series perspective in extant studies on predicting confirmed infectious disease cases [ 2 , 6 , 21 ].

Structures of LSTM (left) and GRU (right).

https://doi.org/10.1371/journal.pone.0309842.g002

The GRU model evolved from LSTM, and it simplifies LSTM to reduce learning time, thus resulting in similar performance but faster data learning [ 37 , 38 ]. Unlike LSTM, GRU has a reset gate and an update gate, where the reset gate calculates the degree of reflection of the previous state (ht), and its role is similar to that of the forget gate. Meanwhile, the update gate determines the rate at which to reflect the previous state (ht) and the current input state ( Fig 2 ). As with LSTM, the GRU model, too, has been used extensively for time-series analysis in recent years, and it has been used in studies on predicting the number of confirmed cases of infectious diseases [ 22 , 24 ].

Research framework

Overall framework.

In this study, data were obtained from Twitter, a social networking service (SNS) where one can freely write their thoughts, Pre-processing and part-of-speech (POS) tagging of these data were performed, and the positive/negative polarities of each tweet were derived daily using a sentiment dictionary. The number of confirmed cases was predicted through ML shown in Fig 3 .

https://doi.org/10.1371/journal.pone.0309842.g003

Data collection and preprocessing

Among various SNS data, the tweet data of Twitter ( https://twitter.com/ ) can be accessed by everyone. Moreover, people can freely express their thoughts on Twitter, and the amount of data on Twitter is adequate for analysis. Owing to these characteristics, this study preemptively found Twitter data to be suitable for use in this study. Tweet data containing keywords related to COVID-19 were extracted from Twitter. Tweet data of 30 months after the first confirmed case of COVID-19 were collected using Python by the collection and analysis method complying with the terms and conditions for the source of the data. The number of COVID-19 confirmed patients used in the study is collected at the Seoul Open Data Plaza ( https://data.seoul.go.kr/ ). Duplicate data were deleted from the collected social data, and news data and promotional posts that did not contain user opinions were excluded. Thereafter, data in Korean only were created through preprocessing, and POS tagging was performed using Kkma.

Opinion mining on social data

This study assumes that the information from social data can influence the spread of infectious diseases and that utilizing this data can lead to more accurate predictions of the number of confirmed cases. Therefore, the proposed methodology employs sentiment analysis of opinion mining to extract meaningful information from the social data. The opinion mining method used herein calculates the polarity of a sentence in terms of the average of polarities from the word perspective to determine the polarity of each text data. To start this process, it is necessary to define a sentiment dictionary to set the polarity of each word. Although a Korean-language sentiment dictionary is available, it has been expanded to match the characteristics of the SNS data collected using the Korean Sentiment Analysis Corpus (KOSAC) Korean sentiment dictionary [ 39 ], which, according to previous studies [ 27 , 40 ], provides better results if a sentiment dictionary is written considering the characteristics of the each document.

In previous studies, the cosine similarity of Word2Vec was used to successfully expand the sentiment dictionary [ 32 – 34 ]. Therefore, in this study, the expansion of the sentiment dictionary using Word2Vec is confirmed to be necessary for better sentiment analysis. Polarities are determined based on the cosine similarity of words corresponding to positive/negative words by using the Word2Vec method. In case of the existing KOSAC Korean sentiment dictionary, each word has a label value for positive/negative as +1 for positive, -1 for negative, and 0 for neutral.

The Word2Vec model learned the collected 1.08 million text data. Between the CBOW and Skip-Bow learning models, we used the Skip-Bow model, which learns more data. This model was trained by setting the minimum number of appearances to 100, which was 0.01% of the amount of text data collected. By using the produced sentiment dictionary, positive/negative words and words with high cosine similarity were extracted by inputting words of sentiment dictionary into the Word2Vec model. Cosine similarity is calculated as shown in Eq1. Studies have demonstrated that a sentiment dictionary can be established successfully when the similarity is 0.5 or higher [ 34 ], and in this study, this study expanded the sentiment dictionary by considering a word an equivalent word with the same positive/negative label when the similarity of the word was 0.8 or higher to ensure high reliability ( Fig 4 ). If a particular word originated from both positive/negative labels, the mean of cosine similarity was checked to provide a more similar positive/negative label.

https://doi.org/10.1371/journal.pone.0309842.g004

The average polarity of each tweet was calculated by substituting the text data with adjectives, verbs, adverbs, nouns, and radix polarities in the produced sentiment dictionary ( Table 2 ). Thereafter, the polarities of the daily text data were collected, and the daily polarity was calculated and used as the input to the model for predicting the number of confirmed patients. The formula for calculating the sentiment value of each tweet is given in Eq2. In Eq2, t represents each tweet, x represents the number of words in t that have sentiment polarities, and w represents the word in set x.

https://doi.org/10.1371/journal.pone.0309842.t002

Predicting number of confirmed cases

Based on successful cases of predicting the number of confirmed cases using machine learning, this study also employs models from the machine learning family (DNN, LSTM, GRU) that have demonstrated high effectiveness [ 2 , 6 , 18 , 21 – 24 ]. In this part, predictions with and without daily positive/negative polarities obtained from opinion mining are compared. First, predictions were generated using the DNN, LSTM, and GRU models by using only the number of confirmed patients per day, and predictions were generated under the same conditions by including the positive/negative polarities. To compare the prediction accuracy in this process, the Mean Absolute Percentage Error (MAPE), which calculates the ratio of the difference between the predicted value and the actual value according to the characteristics of the number of confirmed patients with a large range, was used. To predict the number of confirmed cases of infectious diseases, the DNN, LSTM, and GRU ML models consisting of two hidden layers, as shown in Fig 5 , were applied to finally predict linear values. The data used for prediction were the daily positive/negative polarities extracted in opinion mining on social data part and the data on the number of confirmed patients in Korea. These data were divided in a 7:3 ratio into the learning dataset and verification dataset, and the prediction model was applied to these two datasets. An example of input data is depicted in the blue box in ( Fig 6 ). After predicting the number of confirmed cases on the next day by using the daily number of confirmed cases and positive/negative polarities of n-days before the forecast date, the MAPE values of the actual and predicted values were calculated to measure the prediction accuracy.

https://doi.org/10.1371/journal.pone.0309842.g005

https://doi.org/10.1371/journal.pone.0309842.g006

Before executing the final prediction algorithm, the number of confirmed cases and the daily polarity calculated in opinion mining on social data part, were applied to the model as input values, and the optimal model and duration were confirmed by conducting several experiments. Subsequently, in this study, the predicted number of confirmed cases on the next day obtained by using only the data of the number of confirmed cases and the prediction results obtained using daily polarities are compared to confirm the prediction accuracy ( Fig 5 ). The input data are used as daily polarities, and the number of confirmed cases of n-days before the forecast date and MAPE values are calculated by comparing the predicted and actual values of the next day to confirm the results.

Search terms were collected using a total of five words, including four Corona-related words (“Corona,” “COVID-19,” “COVID-19 confirmed and “COVID-19 Vaccine” based on Google Trends) and “epidemic.” Prior to collecting data for machine learning techniques, this study considered whether a small amount of data could be used. To measure the daily number of confirmed cases of infectious diseases, data from when the epidemic is active should be used, because there were numbers of units that did not fit perfectly in the category of big data. However, recent papers predicting the number of confirmed cases of infectious diseases using machine learning have also been confirmed using a small amount of data like Table 3 . Therefore, although limited in this study, the prediction was conducted using 756 points of data. In addition, fields that require actual infectious disease prediction will also require rapid response, and the model proposed in this study reflects situations in which they are forced to use less data.

https://doi.org/10.1371/journal.pone.0309842.t003

The data-collecting period spanned from February 24, 2020, to March 21, 2022. A total of 1,080,000 data points were obtained after undergoing preprocessing procedures to exclude duplicate or missing information, as well as advertisement messages from the social media site (Twitter). The collected data include both the date and the corresponding text generated. A total of 1,423 data points were gathered on a daily basis, with a standard deviation of 318.23. Furthermore, data regarding the number of confirmed COVID-19 cases in Korea within the aforementioned time frame was also gathered. POS tagging of these text data was performed using a Kkma POS tagger, and finally, the data were produced, as summarized in Table 4 .

https://doi.org/10.1371/journal.pone.0309842.t004

To match the data collected in the KOSAC Korean sentiment dictionary and the social data, a sentiment dictionary was produced using the Word2Vec technique. Before Word2Vec was used, it learned the entire POS-tagged text data summarized in Table 1 .The minimum number of appearances was 100, which accounted for 0.01% of the total sentence data, and the Skip-Bow model was used as the learning method. As the input data, words from the KOSAC sentiment dictionary were inserted, and words with a cosine similarity of 0.8 or higher, derived through Word2Vec, were added to the new sentiment dictionary because they were considered to have the same positive/negative sentiment polarities. To account for the morphemes of the words, an sentiment dictionary comprising nouns, verbs, adverbs, and adjectives was collated, and a total of 3,070 sentiment words and values were finally extracted ( Table 5 ).

https://doi.org/10.1371/journal.pone.0309842.t005

The average of polarities was calculated for each text data collected using the produced sentiment dictionary. The decision was made considering the two methods used to calculate the daily polarity values from the text data polarity values. As illustrated in Fig 7 , Case 1 has positive and negative sentiment polarities from -1 to 1 on each date, and Case 2 uses two input data that are calculated daily by separating texts with positive polarities from those with negative polarities.

• Case 1: Using the average of daily polarities
• Case 2: Using the mean of each positive and negative daily polarities

https://doi.org/10.1371/journal.pone.0309842.g007

The final calculation method was the one that yielded the better prediction results in terms of the number of confirmed infectious disease patients. As a comparative index of the final prediction result, the MAPE values of the predicted and measured values were used, and the results are summarized in Table 6 . In terms of minimum value, the MAPE values were 11.57% in Case 1 and 10.09% in Case 2. Therefore, as indicated by Case 2 in Fig 7 , the method of calculating the polarity by dividing it into positive and negative was adopted. Table 7 summarizes the polarity of each text data, and Table 8 is a normalized table containing the average values obtained by dividing the daily polarity by positive and negative polarities. The daily polarity represents the degree of positive/negative COVID-19-related opinions of users in the text data obtained from SNS on the corresponding date, and it is finally input into the prediction model in the form of Table 8 .

https://doi.org/10.1371/journal.pone.0309842.t006

https://doi.org/10.1371/journal.pone.0309842.t007

https://doi.org/10.1371/journal.pone.0309842.t008

Predicting the number of confirmed cases

In this section, the number of confirmed cases is predicted using DNN, LSTM, and GRU, which are the machine learning models proposed in the research framework. The input values of the model include the number of confirmed cases in Korea between February 24, 2020, and March 21, 2022, which is the period when the number of confirmed cases appeared steadily in Korea; number of confirmed cases; and positive/negative polarities derived through opinion mining. The data were divided in a ratio of 7:3 to obtain the training and verification datasets, and learning was performed. As for the activation function of DNN, the RELU function with the best results was applied after comparing the experimental results of the sigmoid, RELU, and softmax models; the epoch of each model was set to 500, and learning was performed. The results were confirmed using the Adam optimizer, which yielded the best experimental results among the candidate optimizers, namely Root Mean Square propagation(RMSP), Stochastic Gradient Descent(SGD), Adaptive Moment Estimation(Adam), and Nesterov Accelerated Gradient Adam(Nadam).

The prediction results were organized, as shown in Table 9 , depending on whether the daily polarities were included and by considering the scope of data application. Depending on the presence or absence of polarities, the daily polarity data were divided into applied and notapplied. The prediction inclusion period was used to set the number of data matches required to generate predictions based on the prediction date. For example, if the prediction inclusion period was 14, the value of the prediction point was calculated using the data of 14 days, including the day before the prediction point. In this study, 7 days, the average incubation period expected by the Korea Centers for Disease Control and Prevention; 14 days, the longest officially announced incubation period; and 28 days, the period considering the impact of the previous incubation period due to the nature of the epidemic were used. The MAPE, MSE, RMSE, MAE results summarized in Table 9 were expressed as the average of 30 prediction results. The number of confirmed cases of infectious diseases has an exponential characteristic. Therefore, if the results are presented using only error figures such as MSE, RMSE, and MAE, the MAPE value that can be expressed as a ratio of errors is presented in this study because a model that performs prediction well may be judged to be better when the number of confirmed cases is relatively large.

https://doi.org/10.1371/journal.pone.0309842.t009

The study found that the GRU model achieved the lowest error rate value of 10.093%, including polarities, for a 14-day period. This aligns with the expected incubation period for COVID-19 (1–14 days) announced by the Korea Centers for Disease Control and Prevention. Furthermore, for DNN, the data without polarities exhibited greater predictive power ( Fig 8 ). Conversely, the RNN family models—LSTM and GRU—achieved satisfactory prediction outcomes when utilizing data that had polarities (Figs 9 and 10 ). A t-test was performed to compare the accuracy of 100 learning/test runs using LSTM and GRU models on 14-day data. The comparison was done using both data sets, with and without sentiment polarities. The t-tests resulted in p-values of 1.28e-09 for LSTM and 5.92e-153 for GRU. These values indicate that the results obtained from data that included polarities were statistically significantly superior than those obtained from data that excluded polarities. The analysis and evaluation of 100 learning/test runs highlight the strength and reliability of the findings.

DNN results obtained using 14-day data with polarity excluded (left) and included (right).

https://doi.org/10.1371/journal.pone.0309842.g008

LSTM results obtained using 14-day data with polarity excluded (left) and included (right).

https://doi.org/10.1371/journal.pone.0309842.g009

GRU results obtained using 14-day data with polarity excluded (left) and included (right).

https://doi.org/10.1371/journal.pone.0309842.g010

In addition to the t-test, a binomial test was performed to verify the statistical significance of the win/loss information for each trial. This is crucial because the proposed strategy might "lose" more comparisons but still have a lower average, or alternatively, "win" more comparisons in both the 14 days and 28 days settings but have a lower average in the 28 days setting. For the LSTM results over a 14 days period, the model that included polarities won 82 out of 100 comparisons. This result allowed to reject the null hypothesis that the win probabilities of the two models are equal, with a p-value of 6.14e-11. In the 14 days GRU comparison, which demonstrated the best predictive performance, the model including polarities won all 100 comparisons. These results strongly support that the proposed feature is more significant when it comes to the actual model training. This analysis confirms the effectiveness of the proposed strategy and highlights the importance of incorporating polarities into the model for better predictive performance.

This study also compares its results with other research methods. This work selects the ARIMA model, which utilizes machine learning to make predictions based on time series data [ 19 , 20 ]. Prior research has indicated that the ARIMA model outperforms the Holt, Splines, and TBATS models in predicting the number of confirmed cases on weekly intervals [ 20 ]. Hence, in order to assess performance, this study used the approach of forecasting the weekly count of confirmed cases and thereafter comparing the results. The comparison is made by displaying the MAPE values at weekly intervals starting from the initial prediction date [ 20 ]. The ARIMA model, which demonstrated superior accuracy in prior research, is being compared by the results obtained for situations with and without sentiment polarity. The model’s performance is adequate for forecasting the number of COVID-19 cases in Korea and was evaluated using the ARIMA (2,1,3) parameters suggested in [ 41 ]. Table 10 shows the MAPE values for these models during a six-week period starting from the prediction’s initial date. It also presents a comparison of their average values over the entire period. On average, the GRU model outperformed the ARIMA model in terms of MAPE performance, as indicated by the comparison results. In addition, while evaluating the average performance over the entire period, it was found that the GRU model outperformed the ARIMA model (Table 10 ). This study examines the impact of incorporating sentiment polarity on the quality of results. The trials utilizing the ARIMA model also indicate that the results, which incorporate the sentiment polarities, show some improvement. Furthermore, with the exception of the data from Period1, the study consistently validated that the models incorporating GRU and sentiment polarity had superior performance on average. This comparison highlights the significance of taking sentiment polarity into account when making predictions. It demonstrates that the findings obtained by including sentiment polarity had reduced MAPE values, even when it is used in the method of previous studies.

https://doi.org/10.1371/journal.pone.0309842.t010

Implications & discussion

The results of this study indicate whether the qualitative opinions in social data were considered when predicting the number of confirmed infectious disease patients. In addition, the prediction results obtained using various ML models (DNN, LSTM, GRU) are presented. Finally, the best predictive power was obtained when the GRU model was applied to the data that included polarities. Moreover, all RNN family models yielded statistically significantly better predictive results when using the data that included polarities. According to the LSTM and GRU prediction graphs in Figs 9 and 10 obtained using the data that included or excluded polarities, respectively, the predicted values are smooth when the polarities are excluded, but they have trailing graphs. Trailing graphs indicate low efficiency in real environments. Trailing graph responds late to the forecast flow because it is similar to the amount of data immediately preceding it. This can make it difficult to utilize the prediction results. By contrast, when the polarity is included, the graph is relative rough, but it seems to yield a predictive value that is appropriate for the timing. In addition to the MAPE set as the error value, the characteristics of the graph showed more remarkable results. In addition, the results were compared using the ARIMA model among previous research methods, and it was also confirmed that the model with GRU and sentimental polarity showed the best performance. Therefore, according to our study, better predictive are generated by considering the qualitative characteristics of social data in the prediction process. Additionally, in this study, a model was developed to reduce errors in the predicted and measured values of the number of confirmed cases, but it is expected that it will be developed as a more effective model if a model for rise and fall is presented for future purposes.

During the research process, two methods for calculating the daily polarity were proposed to predict the number of confirmed patients. The first method involved viewing all polarities as an average for each day, and the second method involved calculating the positive and negative polarities separately. As a result of the experiment, the average was obtained by dividing the positive and negative polarities, and when this method was applied to the prediction model, the prediction accuracy increased. The reason for the application of this method was that if multiple data were to be combined using the central limit theorem, the value would remain at a certain level, which would reduce the data dimension that could be expressed for each degree. Moreover, the results were superior when multiple data were included. In future studies on opinion mining and sentiment analysis, it will be possible to consider the method of using polarities by dividing positive and negative properties. In this study, when applying opinion mining to social data, only the method that considered the frequency of words in the existing sentiment dictionaries was used. In future research, this part will be supplemented to reflect advanced research on opinion mining methods. Recently, with the advancement of NLP in the opinion mining and sentiment analysis domains, many studies have been conducted. For example, studies that measure polarities of social data through the use of Transformers, including BERT, are actively underway, and if these tools can analyze polarities from various angles and reflect them, more useful and improved research results can be expected.

It was also meaningful to confirm the data period for predicting the number of confirmed cases in this study. The incubation period proposed by the Korea Centers for Disease Control and Prevention was considered to determine the period for including previous data as the input data before generating predictions using the ML model. The Korea Centers for Disease Control and Prevention announced that the average and maximum incubation periods were 7 days and 14 days, respectively. Therefore, this study was conducted for up to 28 days in consideration of the average incubation period of 7 days, longest incubation period of 14 days, and the 14 day period before the infected person was affected. According to the study results, the LSTM and GRU models yielded the best predictions when using 14day data that included polarities. The meaning of 14 days overlaps with the meaning of 2 times the average incubation period of 7 days suggested by the Korea Centers for Disease Control and Prevention and the maximum incubation period of 14 days. These results suggest that further analysis is necessary to determine the significance of the relationship between the incubation period announced by the Centers for Disease Control and Prevention and the use of social data to predict infectious diseases.

In the social data covered intensively in this study, new words or new expressions appear over time owing to the characteristics of language. In this study, this study proposed a method for including these expressions in sentiment analysis by developing an existing sentiment dictionary using Word2Vec. This method can automatically collect data that reflect the changing characteristics of SNS language without needing a qualitative process involving experts. In addition, it is possible to update the sentiment dictionary to reflect the newly emerging language trends and conduct sentiment analysis automatically. This feature ensures that the proposed model can be updated and applied at a certain point in time in the future. In order to utilize the results of this study, users can collect social data containing the degree of positivity to infectious diseases and use the extracted sentiment polarities of each content as a parameter for infectious disease prediction algorithms. In order to extract the sentiment polarity of each data, an sentiment dictionary must be established considering the characteristics of each language, and it is expected that analysis can be performed according to the characteristics of each country and epidemic spread. Predicting the number of confirmed cases of the pandemic will keep individuals alert, enable policymakers to pre-imagine health-related resources and personnel plan, and allow them to move toward a quick end to the pandemic, taking into account when planning a response to preventive measures to prevent it.

Notwithstanding these contributions, it should be noted that the findings being given are applicable only to particular places and circumstances. This study employed qualitative aspects of social data to forecast the number of confirmed instances of infectious illnesses. To ensure accurate utilization, it is important to account for the amount of people engaged in social data and the regional influence of such data. Furthermore, it is important to incorporate variations in language and grammar structures, disparities in social media usage and recognition patterns, as well as cultural norms and frequency of social media engagement across different nations, since these factors can significantly impact social media dynamics and user behavior. This article presents the findings of a research endeavor that involved the development and validation of an epidemic prediction model. The model was constructed by leveraging opinion mining outcomes derived from social data in Korea, a country characterized by dense population and extensive utilization of social network services. In the future, it will be necessary to construct models using opinion mining in various languages and nations.

This study aimed to propose a methodology for predicting the number of confirmed cases of infectious diseases by using opinion mining, which allows for the inclusion of qualitative opinions from social data in epidemic prediction. To this end, about 1 million SNS Twitter data were collected, and the Word2Vec model was learned using the collected social data to expand the existing sentiment dictionary for sentiment analysis. After that, a model was developed to predict the number of confirmed COVID-19 patients by using the calculated sentiment polarities, and predictions were generated. As a result, when predicting using sentiment polarities, the predictive performances of LSTM and GRU increased by 1.12% and 3%, respectively, compared to those when sentiment polarities were not used, and these differences were statistically significant. These results also confirmed the differences through a binomial test for the win/loss of the two model outcomes, and the results were compared using the periodical model comparison method utilized in previous studies. Despite these comparisons, it was shown that using sentiment polarities from social data for prediction is more significant. Additionally, these results indicate that it is possible to predict the number of confirmed cases by continuously monitoring both the number of confirmed cases and the sentiment state.

Through continuous monitoring of social sentiment states, it is possible to develop and adjust policies that reflect changes in public perception. Policymakers can evaluate the effectiveness of policies based on real-time sentiment data and swiftly adjust them as needed to meet public demands. In addition, it is possible to prevent the spread of misinformation and gain public trust. Based on the results of social media sentiment analysis, tailored messages can be crafted and distributed to the public, and communication strategies can be established to promptly counteract misinformation.

However, the study has limitations in terms of the data and models used therein. In the collection of social data, the data of other media and news cannot be included by analyzing only Twitter data. In case of the model, the comparative analysis results presented herein consider only the DNN, LSTM, and GRU ML models. In addition, as an opinion mining method, only sentiment analysis was used considering the appearance frequencies of positive/negative keywords in the sentiment dictionary.

In the future, studies should be to collect large volumes of high quality social data, conduct experiments using predictive models that are based on methods different from those used in this study, and present a model that predicts a week or longer ahead to produce practical results. In addition to sentiment analysis, the opinion methodology can be confirmed through future tasks to derive results by using various recently emerged models, including DL.

This study started with the aim of improving the prediction of the number of confirmed patients by incorporating sentiment polarities from social data. The results confirmed that including polarity allowed for statistically significantly higher accuracy in predictions compared to excluding polarity. While many previous studies relied solely on quantitative social data, this study highlighted the importance of qualitative opinions from social data in predicting the number of confirmed infectious disease patients. Therefore, it underscores the need for further research using social data and opinion mining in the field of infectious disease prediction.

Supporting information

S1 data. collected social data1..

https://doi.org/10.1371/journal.pone.0309842.s001

S1 File. Collected social data2 and Korea’s daily number of confirmed cases.

https://doi.org/10.1371/journal.pone.0309842.s002

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Sentiment analysis: A survey on design framework, applications and future scopes

Monali bordoloi.

1 School of Computer Science and Engineering, VIT-AP University, Inavolu, Amaravati, Andhra Pradesh 522237 India

Saroj Kumar Biswas

2 Computer Science and Engineering Department, NIT Silchar, NIT Road, Silchar, Assam 788010 India

Sentiment analysis is a solution that enables the extraction of a summarized opinion or minute sentimental details regarding any topic or context from a voluminous source of data. Even though several research papers address various sentiment analysis methods, implementations, and algorithms, a paper that includes a thorough analysis of the process for developing an efficient sentiment analysis model is highly desirable. Various factors such as extraction of relevant sentimental words, proper classification of sentiments, dataset, data cleansing, etc. heavily influence the performance of a sentiment analysis model. This survey presents a systematic and in-depth knowledge of different techniques, algorithms, and other factors associated with designing an effective sentiment analysis model. The paper performs a critical assessment of different modules of a sentiment analysis framework while discussing various shortcomings associated with the existing methods or systems. The paper proposes potential multidisciplinary application areas of sentiment analysis based on the contents of data and provides prospective research directions.

Introduction

The advent of digitization accelerated the scope of the general public to express their sentiment or opinion on an online platform. An expert or general public nowadays desires to reach an optimal decision or opinion with the use of available opinionative data. Any online platform, such as an e-commercial website or a social media site, maintains a level of transparency, increasing its chance of influencing other users. However, a single topic or item can possess millions of varied opinions on a single platform. The opinions or sentiments expressed can hold minute details or even a general opinion, which increases the research community’s interest in further investigation. This was the beginning of the principle of sentiment analysis, also known as opinion mining. Sentiment analysis makes it easier to retrieve sentimental details, analyze opinionative/sentimental web data, and classify sentimental patterns in a variety of situations.

Sentiment analysis can be stated as the procedure to identify, recognize, and/or categorize the users’ emotions or opinions for any service like movies, product issues, events, or any attribute as positive, negative, or neutral (Mehta and Pandya 2020 ). When sentiment is stated as a polarity in computational linguistics, it is typically treated as a classification task. When sentiment scores lying inside a particular range are used to express the emotion, the task is however regarded as a regression problem. Cortis et al. ( 2017 ) mentioned various research works where sentiment analysis is approached as either a classification or regression task. While analyzing the sentiments by assigning the instances sentiment scores within the range [− 1,1], Cortis et al. ( 2017 ) discovered that there can be circumstances where the prediction is sometimes considered to be a classification task and other times to be regression. To solve the regression/classification problem, the authors developed a novel approach that combined the use of two evaluation methods to compute the similarity matrix. Therefore, mining and analysis of sentiment are either limited to positive/negative/neutral; or even deeper granular sentimental scale, depending on the necessity, topic, scenario, or application (Vakali et al. 2013 ).

In the last decade since the paper by Pang et al. ( 2002 ), a large number of techniques, methods, and enhancements have been proposed for the problem of sentiment analysis, in different tasks, at different levels. Numerous review papers on sentiment analysis are already available. It has been noted that the current studies do not give the scientific community a comprehensive picture of how to build a proper sentiment analysis model. A general, step-by-step framework that can be used as a guide by an expert or even by a new researcher would be ideal for designing a proper sentiment analysis model. Many of the existing surveys basically report the general approaches, methods, applications, and challenges available for sentiment analysis. The survey paper by Alessia et al. ( 2015 ) reports basic three levels of sentiment analysis, presents three types of sentiment classification approaches, discusses some of the available tools and methods, and points out four domains of applications of sentiment analysis. The study can be further extended to give more details about the different levels, methods/approaches, additional applications, and other related factors and areas. Wankhade et al. ( 2022 ) provided a detailed study of different sentiment analysis methods, four basic levels of sentiment analysis, applications based on domain and industries, and various challenges. The survey emphasizes several classification methods while discussing some of the necessary procedures in sentiment analysis. Instead of only concentrating on the procedures that are necessary for sentiment analysis, a detailed description of all the possible approaches is highly desirable as it can help in selecting the best among all for a certain type of sentiment analysis model. Each step/module of the sentiment analysis model should be discussed in detail to gain insight into which technique should be used given the domain, dataset availability, and other variables; or how to proceed further to achieve high performance. Further, applications of sentiment analysis are commonly described based on the domain or applicable industry. Possible application areas based purely on the dataset are rarely covered by recent review papers. Some of the survey papers focus on only one direction or angle of sentiment analysis. Multimodal sentiment analysis and its applications, as well as its prospects, challenges, and adjacent fields, were the main topics of the paper by Kaur and Kautish ( 2022 ). Schouten and Frasincar ( 2015 ) focused on the semantically rich concept-centric aspect-level sentiment analysis and foreseen the rise of machine learning techniques in this context in the future. Verma ( 2022 ) addressed the application of sentiment analysis to build a smart society, based on public services. The author showed that understanding the future research directions and changes in sentiment analysis for smart society unfolds immense opportunities for elated public services. Therefore, this survey paper aims to categorize sentiment analysis techniques in general, while critically evaluating and discussing various modules/steps associated with them.

This paper offers a broad foundation for creating a sentiment analysis model. Instead of focusing on specific areas, or enumerating the methodological steps in a scattered manner; this paper follows a systematic approach and provides an extensive discussion on different sentiment analysis levels, modules, techniques, algorithms, and other factors associated with designing an effective sentiment analysis model. The important contributions can be summarized as follows:

• The paper outlines all the granularity levels at which sentiment analysis can be carried out, through appropriate representative examples.
• The paper provides a generic step-by-step framework that can be followed while designing a simple as well as a high-quality sentiment analysis model. An overview of different techniques of data collection and standardization, along with pre-processing which significantly influences the efficiency of the model, are presented in this research work. Keyword extraction and sentiment classification having a great impact on a sentiment analysis model is thoroughly investigated.
• Possible applications of sentiment analysis based on the available datasets are also presented in this paper.
• The paper makes an effort to review the main research problems in recent articles in this field. To facilitate the future extension of studies on sentiment analysis, some of the research gaps along with possible solutions are also pointed out in this paper.

The remaining paper is organized into five different sections to provide a clear vision of the different angles associated with a sentiment analysis process. Section 2 provides knowledge of the background of sentiment analysis along with its different granularity levels. A detailed discussion of the framework for performing sentiment analysis is presented in the Sect. 3 . Each module associated with designing an effective sentiment analysis is discussed in this section. Section 4 discusses different performance measures which can be used to evaluate a sentiment analysis model. Section 5 presents various possible applications of sentiment analysis based on the content of the data. Section 6 discusses the future scope of research on sentiment analysis. At last, Sect. 7 concludes the paper.

Background and granularity levels of sentiment analysis

The first ever paper that focused on public or expert opinion was published in 1940 by Stagner ( 1940 ). However, at that time studies were survey based. As reported in Mäntylä et al. ( 2018 ), the earliest computer-based sentiment analysis was proposed by Wiebe ( 1990 ) to detect subjective sentences from a narrative. The research on modern sentiment analysis accelerated in 2002 with the paper by Pang et al. ( 2002 ), where ratings on movie reviews were used to perform machine learning-based sentiment classification. Pang et al. ( 2002 ) classified a document based on the overall sentiment, i.e., whether a review is positive or negative rather than based on the topic.

Current studies mostly concentrate on multilabel sentiment classification, while filtering out neutral opinions/sentiments. Due to the unavailability of proper knowledge of handling neutral opinion, the exclusion of neutral sentiment might lead to disruption in optimal decision-making or valuable information loss. Based on a consensus method, Valdivia et al. ( 2017 ) proposed two polarity aggregation models with neutrality proximity functions. Valdivia et al. ( 2018 ), filtered the neutral reviews using induced Ordered Weighted Averaging (OWA) operators based on fuzzy majority. Santos et al. ( 2020 ) demonstrated that the examination of neutral texts becomes more relevant and useful for comprehending and profiling particular frameworks when a specific polarity pre-dominates. Besides, there can be opinions that usually contain both positive and negative emotions as a result of noise. This kind of opinion is termed an ambivalence opinion, which is often misinterpreted as being neutral. Wang et al. ( 2020 ) presented a multi-level fine-scaled sentiment sensing and showed that the performance of the sentiment sensing improves with ambivalence handling. Wang et al. ( 2014 ) introduced the concept to classify a tweet with more positive than negative emotions into a positive category; and one with more negative emotions than the positive one into a negative sentiment category.

Computational linguistics, Natural Language Processing (NLP), text mining, and text analysis are different areas that are closely interlinked with the sentiment analysis process. The relationship between sentiment analysis and the different areas is summarized below:

Sentiment Analysis is a blend of linguistics and computer science (Taboada 2016 ; Hart 2013 ). Nowadays thousandths of human languages and other abbreviated or special languages exist, say the ones used in social media, which are used to convey thoughts, emotions, or opinions. People might use one single language or a combination of different languages, say for example Hinglish (a combination of Hindi and English) along with emoticons or some symbols to convey their messages. Computational linguistics assists in obtaining the computer-executable and understandable language from the vast source of raw languages through proper representation, to extract the associated sentiments properly. While developing formal theories of parsing and semantics along with statistical methods like deep learning, computational linguistics forms the foundation for performing sentiment analysis.

Linguistics knowledge aids in the development of the corpus set that will be used for sentiment analysis while understanding the characteristics of the data it operates on and determining which linguistic features may be applied. Data-driven or rule-based computer algorithms are designed to extract subjective information or to score polarity with the help of linguistic features, corpus linguistics, computational semantics, part of speech tagging, and the development of analytical systems for parsing. Connotations and associations are used to construct sentiment lexicons.

Recognition of sarcasm, mood classification, and polarity classification are some of the tasks covered by sentiment analysis, which is just a small subset of the discipline of computational linguistics. Approaches to classifying moods introduce a new dimension that is based on external psychological models. Methods for detecting sarcasm make use of ideas like “content” and “non-content” terms, which coexist in linguistic theory. Language models, such as Grice’s well-known maxims, are used to define sarcasm.

NLP deciphers human language and makes it machine understandable. With the aid of NLP, the sentiments behind human-generated online comments, social media posts, blogs, and other information can be processed and represented by patterns and structures that can be used by software to comprehend and implement them. Sentiment analysis can be considered as a subset of NLP which helps users in opinionative/sentimental decision-making.

Different NLP tasks such as tokenization, stemming, lemmatization, negation detection, n-gram creation, and feature extraction aid in proper sentiment analysis. NLP-based pre-processing helps in improving the polarity classifier’s performance by analyzing the sentiment lexicons that are associated with the subject (Chong et al. 2014 ). As a result, NLP facilitates text comprehension, accurately captures text polarity, and ultimately facilitates improved sentiment analysis (Rajput 2020 ; Solangi et al. 2018 ).

Advanced NLP techniques are often needed when dealing with emoticons, multilingual data, idioms, sarcasm, sense or tone, bias, negation, etc. Otherwise, the outcome can drastically deteriorate. If the NLTK’s general stopwords list is utilized, words like not, nor, and no, for instance, are frequently deleted when removing stopwords during pre-processing. However, the removal of such words can alter the actual sentiment of the data. Thus, depending on its application, NLP tasks can either improve or deteriorate the result.

Text messages, comments, reviews, and blog posts are excellent sources of sentimental information. The extraction of useful information and knowledge hidden in textual data is an important aspect of sentiment analysis. Mining the relevant information from textual data possesses multi-dimensional advantages such as improved decision-making, public influence, national security, health and safety, etc. (Zhang et al. 2021 ; Wakade et al. 2012 ). Text mining involves the use of statistical techniques to retrieve quantifiable data from unstructured text, and uses NLP to transform the unstructured text into normalized, structured data, which makes it suitable for sentiment analysis.

Sentiment analysis, however, is not just confined to text. In most cases, such as when a sarcastic comment is made, or while pointing a finger at someone and saying- “You are responsible!”, the exact sentiment behind the plain text might not be conveyed properly. Non-text data like video, audio, and image are helpful in such a scenario to portray sentiment accurately.

A key part of sentiment analysis is extracting insightful information, trends, and patterns. To extract them from unstructured and semi-structured text data, text analysis is a process that supports sentiment analysis. Using techniques including word spotting, manual rule usage, text classification, topic modeling, and thematic analysis, the procedure helps in the extraction of meaning from the text. Text analysis can be used to specify individual lexical items (words or phrases) and observe the pattern.

Sentiment analysis, in contrast to basic text analytics, fundamentally shows the emotion concealed beneath the words, while text analytics analyses the grammar and relationships between words. Sentiment analysis essentially identifies whether a topic conveys a positive, negative, neutral, or any other sentiment; while text analysis is used to identify the most popular topics and prevalent ideas-based texts. In addition, it can be more challenging to specify the intended target in the context of sentiment conveyed, than it is to determine a document’s general subject.

A textual document with numerous opinions would have a mixed polarity overall, as opposed to having no polarity at all (being objective). It is also important to distinguish the polarity and the strength of a conveyed sentiment. One may have strong feelings about a product being decent, average, or awful while having mild feelings about a product being excellent (due to the possibility that one just had it for a brief period before having an opinion.). Also, unlike topical (involving text) analysis, in many cases such as that of the quotes, it is critical to understand whether the sentiment conveyed in the document accurately reflects the author’s true intentions or not.

Analyzing the existence of an important word in conjunction with the use of a sentiment score approach can help to uncover the most profound and specific insights that can be used to make the best decision in many situations. Areas of application for sentiment analysis aided by appropriate text analysis include strategic decision-making, product creation, marketing, competition intelligence, content suggestion, regulatory compliance, and semantic search.

Granularity levels

At present, a sentiment analysis model can be implemented at various granular levels according to the requirement and scope. There are mainly four levels of sentiment analysis that have gained a lot of popularity. They are document level (Pang et al. 2002 ; Li and Li 2013 ; Hu and Li 2011 ; Li and Wu 2010 ; Rui et al. 2013 ; Zhan et al. 2009 ; Yu et al. 2010 ), sentence or phrase level (Nguyen and Nguyen 2017 ; Wilson et al. 2005 ; Narayanan et al. 2009 ; Liu et al. 2013 ; Yu et al. 2013 ; Tan et al. 2012 ; Mullen and Collier 2004 ), word level (Nielsen 2011 ; Dang et al. 2009 ; Reyes and Rosso 2012 ; Bollegala et al. 2012 ; Thelwall and Buckley 2013 ; Li et al. 2014 ), and entity or aspect level (Li et al. 2012 ; Li and Lu 2017 ; Quan and Ren 2014 ; Cruz Mata et al. 2013 ; Mostafa 2013 ; Yan et al. 2015 ; Li et al. 2015a ).

Some of the other research works concentrate on concept level (Zad et al. 2021 ; Tsai et al. 2013 ; Poria et al. 2013 ; Balahur et al. 2011 ; Cambria et al. 2022 ; Cambria 2013 ), link/user level (Rabelo et al. 2012 ; Bao et al. 2013 ; Tan et al. 2011 ), clause level (Kanayama and Nasukawa 2006 ; Liu et al. 2013 ), and sense level (Banea et al. 2014 ; Wiebe and Mihalcea 2006 ; Alfter et al. 2022 ) sentiment analysis. Some of the important levels of sentiment analysis are discussed in the following sub-sections. To understand the different levels, let us consider a customer review R as shown below.

R = “I feel the latest mobile from iPhone is really good. The camera has an outstanding resolution. It has a long battery life. I can even bear the mobile’s heating problem. However, I feel it could have been a bit light weighted. Given the configurations, it is a bit expensive; but I must give a thumbs up for the processor.”

In the following subsections, we will observe the analysis of review R based on different levels.

Document-level sentiment analysis

It aims to assess a document’s emotional content. It assumes that the overall document expresses a single sentiment (Pang et al. 2002 ; Hu and Li 2011 ). The general approach of this level is to combine the polarities of each word/sentence in the document to find the overall polarity (Kharde and Sonawane 2016 ). According to document-level sentiment analysis, the overall sentiment of the document represented by review R is positive. According to Turney ( 2002 ), there are two approaches to document sentiment classification namely term-counting and machine learning. Term counting measure derives a sentiment measure while calculating total positive and negative terms in the document. Machine learning approaches generally yield superior results as compared to term-counting approaches. In this approach, it is assumed that the document is focused on only one object and thus holds an opinion about that particular object only. Thus, if the document contains opinions about different objects, this approach is not suitable.

Sentence/phrase-level sentiment analysis

The sentiment associated with each sentence of a set of data is analyzed at this level of sentiment analysis. The general approach is to combine the sentiment orientation of each word in a sentence/phrase to compute the sentiment of the sentence/phrase (Kharde and Sonawane 2016 ). It attempts to classify a sentence as conveying either positive/negative/neutral/mixed sentiment or as a subjective or objective sentence (Katrekar and AVP 2005 ). Objective sentences are facts and do not convey any sentiment about an object or entity. They do not play any role in polarity determination and thus need to be filtered out (Kolkur et al. 2015 ). The polarity of a sentence in review R is found to be positive/negative/mixed irrespective of its overall polarity.

Word-level sentiment analysis

Through proper examination of the polarity of each and every word, this sentiment analysis level investigates how impactful individual words can be on the overall sentiment. The two methods of automatically assigning sentiment at this level are dictionary-based and corpus-based methods (Kharde and Sonawane 2016 ). According to Reyes and Rosso ( 2012 ), in corpus-based techniques, the co-occurrence patterns of words are used for sentiment determination. However, most of the time, statistical information needed for the determination of a word’s sentiment orientation is large corpus dependent. The dictionary-based approaches use synonyms, antonyms, and hierarchies from lexical resources such as WordNet and SentiWordNet (SWN) to determine the sentiments of words (Kharde and Sonawane 2016 ). Such techniques assign positive, negative, and objective sentiment scores to each synset. If the words in review R such as outstanding, expensive, etc. are evaluated individually, different words within a particular sentence are observed to hold different polarities.

Aspect or entity-level sentiment analysis

For a specific target entity, this approach essentially identifies various aspects associated with it. Then, the sentiment expressed towards the target by each of its aspects is determined in this level of sentiment analysis. As a result, it can be divided into two different tasks, namely extraction of aspects and sentiment classification of aspects (Liu and Zhang 2012 ). For the different aspects such as resolution, weight, and price of the same product in review R, different sentiments are conveyed.

Concept-level sentiment analysis

Most of the time, merely using emotional words to determine sentiment or opinion is insufficient. To obtain the best results, a thorough examination of the underlying meaning of the concepts and their interactions is required. Concept-level sentiment analysis intends to convey the semantic and affective information associated with opinions, with the use of web ontologies or semantic networks (Cambria 2013 ). Rather than simply using word-cooccurrences or other dictionary-based approaches as in word-level sentiment analysis, or finding overall opinion about a single item as in document-level sentiment analysis; concept-level sentiment analysis generally makes use of feature spotting and polarity detection based on different concepts. E.g., For “long battery life” in review R is considered positive. However, a “long route” might not be preferable if someone wants to reach the destination in minimum time, and thus can be considered as negative. Tsai et al. ( 2013 ) made use of features of the concept itself as well as features of the neighboring concepts.

User-level sentiment analysis

User–level sentiment analysis takes into account the fact that if there is a strong connection among users of a social platform, then the opinion of one user can influence other users. Also, they may hold similar sentiments/opinions for a particular topic (Tan et al. 2011 ). At the user level, all the followers of the reviewer of review R may get influenced by this review.

Clause-level sentiment analysis

A sentence can be a combination of multiple clauses, each conveying different sentiments. The clauses in review R can be observed to represent opposing polarity because they are separated by the word “but”. Clause-level sentiment analysis focuses on the sentiment associated with each clause based on aspect, associated condition, domain, grammatical dependencies of the words in the clause, etc.

Sense-level sentiment analysis

The words which form a sentence can interpret different meanings based on their usage in the sentence. Specifically, when the same word has multiple meanings, the sense with which the word is used, can highly affect the sentiment orientation of the whole sentence or document. E.g., let us consider the word “bear” in review R. Is the word bear referring to the mammal bear? Otherwise, is it indicating the bearing (holding) of something? In what sense it is used? Is it used as a noun or a verb? In such a case, proper knowledge of the grammatical structure or word sense can contribute immensely to the determination of the appropriate sentiment of any natural language text. Thus, solving words’ syntactic ambiguity and performing word sense disambiguation (Wiebe and Mihalcea 2006 ) are vital parts of designing an advanced sentiment analysis model. Alfter et al. ( 2022 ) provided a sense-level annotated resource rather than word-level annotation and performed various experiments to explore the explanations of difficult words.

The analysis of the review R at different levels shows that the same review can have different interpretations based on the requirement. Single-level approaches work well in most cases. However, sometimes when the evaluation of sentiments is based on very short document(s) or even very long document(s), the model may fail to handle the flexibility. To determine the polarity of the overall documents, Li et al. ( 2010 ) combined phrase-level and sentence-level sentiment analysis to design a multi-level model. Valakunde and Patwardhan ( 2013 ) advised following a ladder-like computation. In this technique, aspect or entity-level sentiment is employed to compute the sentence-level sentiments and then use the weightage of entities along with the sentence-level sentiments for evaluation of the complete document.

General framework of sentiment analysis

The evolution of sentiment analysis marks the emergence of different models by different experts. After going through more than 500 sentiment analysis models proposed till now, a general framework of sentiment analysis is presented in Fig.  1 . The framework comprises mainly four modules along with an additional optional module. The modules perform collection and standardization of data; pre-processing of the dataset; extraction of features or keywords which represent the overall dataset; prediction or classification of the sentiments associated with the keywords or the whole sentence or document according to the requirement; and summarization of the overall sentiment associated with the dataset. The different modules are discussed in detail below.

Data collection and standardization

With the growing platforms of expression, the type and format of expressing people’s views, opinions, or sentiments on a particular subject is increasing. Among the different available types of data such as text, image, audio, or video, the research on textual data has gained momentum in the last few years. Currently, though multi-lingual text data has attracted few researchers, however, 90% of sentiment analysis studies, experimentation, and design concentrates mainly on English textual data.

The development, examination, and validation of a system typically depend on the quality and structure of data used for building, operating, and maintaining the model. The overall functionality of a model depends on the data used from the boundless and voluminous source of available data to a great extent. Many public data sources are available which are used by some researchers to design a sentiment analysis model. Publicly available dataset namely Blitzer’s multi-domain sentiment data (Blitzer et al. 2007 ) is used by Dang et al. ( 2009 ). Public product reviews by Epinions (epinions.com) are also used by some of the researchers (Kharde and Sonawane 2016 ; Fahrni and Klenner 2008 ). UCI Machine Learning Repository provides standard datasets for sentiment namely Twitter data for Arabic Sentient Analysis, Sentiment Labelled Sentences, Paper Reviews, Sentiment Analysis in Saudi Arabia about distance education during Covid-19, etc. The overwhelming rate of data production demands designing a system that keeps on updating the database from time to time to avoid generality or biased interest at a particular time. A manual approach to collecting a substantial volume of data is not a desirable practice. Thus, automatic big data collection techniques are indeed a vital aspect that must be keenly observed. Several tools or APIs have come up recently that help to collect data from online social or e-commercial platforms. Some of them are NodeXL, Google spreadsheet using Twitter Achiever, Zapier, Rapid Miner, Parsehub, BeautifulSoup in Python, WebHarvy, etc. Most of these tools or APIs help to collect real-time data. But the main problem occurs when someone desires to work with historical data; because many of these techniques such as Twitter API do not permit extracting tweets older than seven days. Building a standard database involves dealing with the unstructured information attached to the data from the internet. For a dataset representing a particular topic, proper standardization in an appropriate type, format, and context, extensively boosts the overall outcome of the analysis. To design a robust system, the homogeneity of the data must be maintained. Besides, proper labelling of the collected data can improve the performance of the sentiment analysis model. Different online labelling techniques are available nowadays. However, online labelling techniques are sometimes full of noise, which leads to lower accuracy of the system. Designing an automatic labelling system, which makes use of various statistical knowledge of the whole corpus and appropriate domain knowledge of words, proves to contribute more to enhancing the sentiment analysis process.

Pre-processing

The process of removing any sort of noise from a textual dataset and preparing a cleaned, relevant and well-structured dataset for the sentiment analysis process is called as pre-processing. Appropriate pre-processing of any dataset noticeably improves the sentiment analysis process. For analyzing the sentiment of online movie reviews, a three-tier approach is adopted by Zin et al. ( 2017 ) to examine the effect of pre-processing task. In the first tier, they experimented with the removal of stopwords using the English stopwords list. The stopwords are the words such as the articles a, an, the, etc., which have no effective role in determining sentiment. In the second tier, the sentiment analysis is performed after the removal of stopwords and all other meaningless characters/words such as date (16/11/20), special characters (@, #), and words with no meaning (a+, a-, b+). In the third tier, more cleaning strategies are used, i.e., numbers and words having less than three characters are removed along with the stopwords and meaningless words. Their results demonstrate that the different combinations of the pre-processing steps show favorable improvement in the classification process; thus, establishing the significance of the removal of stopwords, meaningless words such as special characters, numbers, and words with less than three characters. Jianqiang ( 2015 ) found that replacing negations, and expanding acronyms have a positive effect on sentiment classification, however, the removal of URLs, numbers, and stopwords hardly changes the accuracy. Efficient pre-processing can increase the accuracy of a sentiment analysis model. To establish it, Haddi et al. ( 2013 ) combined various pre-processing methods using online reviews of movies and followed different steps such as cleaning online text, removal of white space, expansion of abbreviations, stemming, eliminating stopwords, and handling negation. Apart from these, they also considered feature selection as a pre-processing step. They used the chi-square method to filter out the less impactful features. To handle negation, a few researchers such as Pang et al. ( 2002 ), used the following words to tag the negation word until a punctuation mark occurs. However, authors of Haddi et al. ( 2013 ) and Dave et al. ( 2003 ) observed that the results before and after the tagging remain almost the same. Therefore, Haddi et al. ( 2013 ) reduced the number of tagged following words to three and two. Saif et al. ( 2014 ) observed that a list of pre-complied stopwords negatively affects Twitter sentiment classification. However, with the use of pre-processing the original feature space is significantly reduced. Jianqiang and Xiaolin ( 2017 ) show that stopword removal, acronym expansion, and replacing negation are effective pre-processing steps. According to Jianqiang and Xiaolin, URLs and numbers do not contain useful information for sentiment analysis. They also found that reverting words with repeated characters shows fluctuating performance. This must be because, in some situations, a word such as goooood gets replaced by goood. Thus, creating confusion about whether it should be interpreted as good or god. Such a situation may alter the actual polarity conveyed by the word. Therefore, reverting words with repeated characters is not recommendable.

Feature/keyword extraction

In a sentiment analysis model, the words and symbols within the corpus are mainly used as the features (O’Keefe and Koprinska 2009 ). Traditional topical text classification approaches are used in most sentiment analysis systems, in which a document is treated as a Bag of Words (BOW), projected as a feature vector, and then categorized using a proper classification technique. Experts use a variety of feature sets to boost sentiment classification efficiency, including higher-order n-grams (Pang et al. 2002 ; Dave et al. 2003 ; Joshi and Rosé 2009 ), word pairs and dependency relations (Dave et al. 2003 ; Joshi and Rosé 2009 ; Gamon 2004 ; Subrahmanian and Reforgiato 2008 ). Using different word-relation feature sets namely unigram (one word), bigram (two words), and dependency parsing, Xia et al. ( 2011 ) performed sentiment classification using an ensemble framework. Wiebe and Mihalcea ( 2006 ) introduced a ground-breaking study focused on the Measure of Concern (MOC) to assess public issues using Twitter data and the most significant unigrams. While conducting text opinion mining, Sidorov et al. ( 2013 ) demonstrated the supremacy of unigrams, as well as other suitable settings such as minimal classes, the efficacy of balanced and unbalanced corpus, the usage of appropriate machine learning classifiers, and so on. Every word present in a dataset is not always important in the context of sentiment analysis. The difficulty of determining precise sentiment classifications has been increased by the continuous growth of knowledge. Even after cleaning the dataset with various pre-processing steps, using all of the data in the dataset can result in dimensionality issues, longer computation times, and the use of irrelevant or less significant features or terms. Especially in the case of higher dimensional and multivariate data, these problems become even worse. According to Li et al. ( 2017 ), a good word representation that captures sentiment is good at word sentiment analysis and sentence classification; and building document-level sentiment analysis dynamically based on words in need is the best practice. Keyword extraction is a method for extracting essential features/terms from textual data by defining particular terms, phrases, or words from a document to represent the document concisely (Benghuzzi and Elsheh 2020 ). If a text’s keywords are extracted correctly, the text’s subject can be thoroughly researched and evaluated, and a good decision can be made about the text. Given that, manually extracting keywords from such a large number of databases is a repetitive, time-consuming, and costly process, automated keyword extraction has become a popular field of research for most researchers in recent years. Automatic keyword extraction can be categorized into supervised, semi-supervised, and unsupervised methods (Beliga et al. 2015 ). The keywords are mainly represented using either Vector Space Model (VSM) or a Graph-Based Model (GBM) (Ravinuthala et al. 2016 ; Kwon et al. 2015 ). Once the datasets are represented using any of the VSM or GBM techniques, the keywords are extracted using simple statistics, linguistics, machine learning techniques, and hybridized methods (Bharti and Babu 2017 ). Simple methodologies that do not include training data and are independent of language and domain are included in the statistical keyword extraction methods. To identify keywords, researchers used frequency of terms, Term Frequency-Inverse Document Frequency (TF-IDF), co-occurrences of terms, n-gram statistics, PATricia (PAT) Tree, and other statistics from documents (Chen and Lin 2010 ). The linguistic approach examines the linguistic properties of words, sentences, and documents, with lexical, semantic, syntactic, and discourse analysis being the most frequently studied linguistic properties (HaCohen-Kerner 2003 ; Hulth 2003 ; Nguyen and Kan 2007 ). A machine learning technique takes into account supervised or unsupervised learning while extracting keywords. Supervised learning produces a system that is trained on a collection of relevant keywords followed by identification and analysis of keywords within unfamiliar texts (Medelyan and Witten 2006 ; Theng 2004 ; Zhang et al. 2006 ). All of these methods are combined in the hybrid method for keyword extraction. O’Keefe and Koprinska ( 2009 ) performed sentiment analysis using machine learning classifiers, which they validated using the movie review dataset. Along with the use of feature presence, feature frequency, and TF-IDF as feature weighting methods, they proposed SWN Word Score Groups (SWN-SG), SWN Word Polarity Groups (SWN-PG), and SWN Word Polarity Sums (SWN-PS) using words which are grouped by their SWN values. The authors suggest categorical Proportional Difference (PD), SWN Subjectivity Scores (SWNSS), and SWN Proportional Difference (SWNPD) as feature selection techniques. They discovered that feature weights based on unigrams, especially feature presence, outperformed SWN-based methods. Using different machine learning techniques; Tan and Zhang ( 2008 ) proposed a model for sentiment analysis in three domains: education, film, and home, which was written in Chinese and used various feature selection techniques for the purpose. Mars and Gouider ( 2017 ) proposed a MapReduce-based algorithm for determining opinion polarity using features of consumer opinions and big data technologies combined with Text Mining (TM) and machine learning tools. Using a supervised approach, Kummer and Savoy ( 2012 ) suggested a KL score for providing weightage to features for sentiment and opinion mining. All these research works establish that the machine learning approach of keyword extraction when incorporated with any other techniques has a great scope in the field of sentiment analysis. There are different kinds of methods that are used to perform keyword extraction using VSM and GBM approaches. They are discussed in detail below.

Vector space model

In VSM, the documents are represented as vectors of the terms (Wang et al. 2015 ). VSM involves building a matrix V which is usually termed as a document-term matrix, where the rows represent the documents in the dataset, whereas columns correspond to the terms of the whole dataset. Thus, if the set of documents is represented by D = ( d 1 , d 2 , . . . . , d m ) and the set of terms/tokens representing the entire corpus is T = ( t 1 , t 2 , . . . . , t n ) , then the element d t i , j ∈ V mxn , i = 1 , 2 , … , m , and j = 1 , 2 , … , n is assigned a weight w i , j . The weights can be assigned based on the word frequency associated with a document or the entire dataset. According to Abilhoa and De Castro ( 2014 ), the frequencies can be binary, absolute, relative, or weighted. Algorithms such as binary, Term Frequency (TF), TF–IDF, etc. are used in traditional term weighting schemes.

If document d i contains the term t j , the element d t i , j of a term vector is assigned a value 1 in the binary term weighting scheme, otherwise, the value 0 is assigned (Salton and Buckley 1988 ). It has the obvious drawback of being unable to recognize the most representative words in a text. Furthermore, using word frequency often helps to increase the importance of terms in documents.

The limitation of the binary term weighting scheme motivates the use of term frequency as the weight of a term for a specific text. The number of times a word appears in a text is known as its term frequency. As a result, a value w i , j is assigned to d t i , j with w i , j equaling the number of times the word t j appears in the document d i . However, as opposed to words that appear infrequently in documents, terms that appear consistently in all documents have less distinguishing power to describe a document (Kim et al. 2022 ). This is an area where the TF algorithm falls short.

The number of documents in the entire document corpus where a word appears is known as its document frequency. If a word has a higher document frequency, it has a lower distinguishing power, and vice versa. As a result, the Inverse Document Frequency (IDF) metric is used as a global weighting factor to highlight a term’s ability to identify documents. Equation  1 (Zhang et al. 2020 ) may be used to describe a term’s TF-IDF weight as follows:

where, t f k denotes the frequency of the term t k in a specific document and d f k denotes the document frequency of the term t k , i.e., the number of documents containing the term t k . The total number of documents in the corpus is denoted by m .

Using the traditional term-weighing techniques, many experts tried to propose their improvised version. Some of them are TF-CHI (Sebastiani and Debole 2003 ), TF-RF (Lan et al. 2008 ), TF-Prob (Liu et al. 2009 ), TF-IDF-ICSD (Ren and Sohrab 2013 ), and TF-IGM (Chen et al. 2016 ).

Graph based model

A graph G is constructed in GBM, with each node or vertex V i representing a document term or function t i and the edges E i , j representing the relationship between them (Beliga et al. 2015 ). Nasar et al. ( 2019 ) showed that various properties of a graph, like centrality measures, node’s co-occurrence, and others, play a significant role in keyword ranking. Semantic, syntactic, co-occurrence, and similarity relationships are some of the specific perspectives of graph-based text analysis. In GBM techniques, centrality measures tend to be the most significant deciding factor (Malliaros and Skianis 2015 ). The importance of a term is calculated by using the centrality measure, to calculate the importance of the node in the graph. Beliga ( 2014 ) presented the knowledge of nineteen different measures which are used for extraction purposes. Degree centrality, closeness centrality, betweenness centrality, selectivity centrality, eigenvector centrality, PageRank, TextRank, strength centrality, neighborhood size centrality, coreness centrality, clustering coefficient, and other centrality measures have been proposed so far. Some of the popular centrality measures are discussed below.

Degree centrality is used to measure how often a term occurs with any other term. For a particular node, the total count of edges incident on it is used to measure the metric (Beliga 2014 ). The more edges that cross the node, the more significant it is in the graph. A node V i ’s degree centrality is measured using Eq.  2 .

where, D C ( V i ) represents node V i ’s degree centrality, ∣ N ∣ indicates the total count of nodes and ∣ n ( V i ) ∣ represents the overall nodes linked with the node V i .

Closeness centrality determines the closeness of a term with all other terms of the dataset. This metric calculates the average of the shortest distance from a given node to every other node in the graph. It is defined by Eq.  3 (Tamilselvam et al. 2017 ) as the reciprocal of the number of all node distances to any node, i.e. the inverse of farness.

where, C C ( V i ) represents node V i ’s closeness centrality, ∣ N ∣ represents graph’s node count, and d i s t ( V i , V j ) represents the shortest distance from node V i to node V j .

This metric is used to see how often a word appears in the middle of another term. This metric indicates how many times a node serves as a bridge between two nodes on the shortest path. For a node V i , it is calculated using Eq.  4 (Tamilselvam et al. 2017 ).

In Eq.  4 , B C ( V i ) represents V i ’s betweenness centrality, σ V x V y represents the overall shortest paths from node V x to V y , and the overall shortest paths from node V x to V y via. V i is represented by σ V x V y ( V i ) .

Selectivity Centrality ( S C ( V i ) ) (Beliga et al. 2015 ) is the average weight on a node’s edges. As shown in Eq.  5 , S C ( V i ) is equal to the fraction of strength of node s ( V i ) to its degree d ( V i ) .

As shown in Eq.  6 , node V i ′ s strength, s ( V i ) , is the summation of overall edge weights incident on ( V i ) .

This centrality measure determines the global importance of a term. It is calculated for a node using the centralities of the neighbors of the node. It is calculated using the adjacency matrix and a matrix calculation to determine the principal eigenvector (Golbeck 2013 ). Assume that A is a ( nxn ) similarity matrix, with A = ( α V i V j ) , α V i V j = 1 if V i is bound to V j and α V i V j = 0 , otherwise. The i-th entry in the normalized eigenvector belonging to the largest eigenvalue of A is then used to describe the eigenvector centrality E V C ( V i ) of node V i . Equation  7 (Bonacich 2007 ) shows the formula for eigenvector centrality.

where, λ is the largest eigenvalue of A . Castillo et al. ( 2015 ) suggested a supervised model with the use of degree and closeness centrality measures of a co-occurrence graph, to determine words belonging to each sentiment while representing existing relationships among document terms. Nagarajan et al. ( 2016 ) have also suggested an algorithm for the extraction of keywords based on centrality metrics of degree and closeness. For obtaining the optimal set of ranked keywords, Vega-Oliveros et al. ( 2019 ) used nine popular graph centralities for the determination of keywords and introduced a new multi-centrality metric. They found that all of the centrality measures have a strong relationship. The authors also discovered that degree centrality is the quickest and most efficient measure to compute. While experimenting with various centrality measures, Lahiri et al. ( 2014 ) also noticed that degree centrality makes keyword and key extraction much simpler. Abilhoa and De Castro ( 2014 ) suggest a keyword extraction model based on graph representation, and eccentricity and closeness centrality measures. As a tiebreaker, they used the degree centrality. In several real-world models, disconnected graphs are common, and using eccentricity and closeness centralities to achieve the expected result often fails. Yadav et al. ( 2014 ) recommended extracting keywords using degree, eccentricity, closeness, and other centralities of the graph while emphasizing the semantics of the terms. With the use of Part of Speech (PoS) tagging, Bronselaer and Pasi ( 2013 ) presented a method to represent textual documents in a graph-based representation. Using various centralities, Beliga et al. ( 2015 ) proposed a node selectivity-driven keyword extraction approach. Kwon et al. ( 2015 ) suggested yet another ground-breaking keyword weighting and extraction method using graph. To improvise the traditional TextRank algorithm, Wang et al. ( 2018 ) used document frequency and Average Term Frequency (ATF) to calculate the node weight for extraction of keywords belonging to a particular domain. Bellaachia and Al-Dhelaan ( 2012 ) introduced the Node and Edge rank (NE-rank) algorithm for keyword extraction, which basically combines node weight (i.e., TF-IDF in this case) with TextRank. Khan et al. ( 2016 ) suggested Term-ranker, which is a re-ranking approach using graph for the extraction of single-word and multi-words using a statistical method. They identified classes of semantically related words while estimating term similarity using term embedding, and used graph refinement and centrality measures for extraction of top-ranked terms. For directed graphs, Ravinuthala et al. ( 2016 ) weighted the edges based on themes and examined their framework for keywords produced both automatically and manually. Using the PageRank algorithm, Devika and Subramaniyaswamy ( 2021 ) extracted keywords based on the graph’s semantics and centralities. The above studies show that centrality measures are a catalyst for effective sentiment analysis. This is because a powerful keyword’s effect or position in determining the sentiment score is often greater than a weaker keyword. For the extraction of sentiment sentences, Shimada et al. ( 2009 )suggested the use of a hierarchical acyclic-directed graph and similarity estimation. For sentences’ sentiment representation, Wu et al. ( 2011 ) developed an integer linear programming-based structural learning system using graph. Using graphs, Duari and Bhatnagar ( 2019 ) also suggested keyword’s score determination and extraction procedures based on the sentences’ cooccurrence with a window size set to 2, position-dependent weights, contextual hierarchy, and connections based on semantics. In comparison to other existing models, their model has an excessively high dimensionality with terms in the text interpreted as nodes and edges representing node relationships in the graph. A variety of unsupervised graph-driven automated keyword extraction approaches is investigated by Mothe et al. ( 2018 ) using node ranking and varying word embedding and co-occurrence hybridization. Litvak et al. ( 2011 ) suggested DegExt, an unsupervised cross-lingual keyphrase extractor that makes use of syntactic representation of text using graphs. Order-relationship between terms represented by nodes is represented by the edges of such graphs. However, without a restriction on the maximum number of possible nodes which can be used, their algorithm generates exponentially larger graphs with larger datasets. As a result, dimensionality is one of the consequences of a graph-based keyword extraction procedure that must be regulated using appropriate means for sentiment analysis to be efficient. Chen et al. ( 2019 ) suggested extracting keywords using an unsupervised approach that relied solely on the article as a corpus. Words are ranked in their model based on their occurrence in strong motifs. Bougouin et al. ( 2013 ) assessed the relevance of a document’s topic in order to suggest TopicRank, an unsupervised approach for extracting key phrases. However, it should be mentioned that their model does not have the optimal key selection approach. To retrieve topic-wise essential keywords, Zhao et al. ( 2011 ) suggested a three-stage algorithm. Edge-weighting is used to rate the keywords (i.e., nodes) using two words’ co-occurrence frequency, followed by generation as well as the ranking of candidate keyphrases. Shi et al. ( 2017 ) suggested an automated single document keyphrase extraction technique based on co-occurrence-based knowledge graphs, which learns hidden semantic associations between documents using Personalized PageRank (PPR). Thus, many experts have used co-occurrence graphs, as well as other graph properties such as centrality metrics, to demonstrate the effectiveness of these methods for keyword ranking in sentiment analysis.

Sentiment prediction and classification techniques

Different techniques have emerged till now for serving sentiment prediction and classification purposes. Several researchers group the techniques based on the applicability of the techniques, challenges, or simply the general topics of sentiment analysis. According to Cambria ( 2016 ), affective computing can be performed either by using knowledge-based techniques, statistical methods, or hybrid approaches. Knowledge-based techniques categorize text into affect categories with the use of popular sources of affect words or multi-word expressions, based on the presence of affect words such as ‘happy’, ‘sad’, ‘angry’ etc. Statistical methods make use of affectively annotated training corpus and determine the valence of affect keywords through word co-occurrence frequencies, the valence of other arbitrary keywords, etc. Hybrid approaches such as Sentic Computing (Cambria and Hussain 2015 ) make use of knowledge-driven linguistic patterns and statistical methods to infer polarity from the text.

Medhat et al. ( 2014 ) presented different classification techniques of sentiment analysis in a very refined and illustrative manner. Inspired by their paper, the current sentiment prediction and classification techniques are depicted in The evolution of sentiment analysis marks the emergence of different models by different experts. After going through more than 500 sentiment analysis models proposed till now, a general framework of sentiment analysis is presented in Fig.  2 . The framework comprises mainly four modules along with an additional optional module. The modules perform collection and standardization of data; pre-processing of the dataset; extraction of features or keywords which represent the overall dataset; prediction or classification of the sentiments associated with the keywords or the whole sentence or document according to the requirement; and summarization of the overall sentiment associated with the dataset. The different modules are discussed in detail below. The techniques are examined thoroughly below, to assist in choosing the best sentiment analysis classification or prediction method for a particular task.

Sentiment classification techniques

Machine learning approach

The machine learning approach of sentiment classification uses well-known machine learning classifiers or algorithms along with linguistic features to classify the given set of data into appropriate sentiment classes (Cambria and Hussain 2015 ). Given a set of data, machine learning algorithms focus to build models which can learn from the representative data Patil et al. ( 2016 ). The extraction and selection of the best set of features to be used to detect sentiment are crucial to the models’ performance Serrano-Guerrero et al. ( 2015 ). There are basically two types of machine learning techniques namely supervised and unsupervised. However, some researchers also use a hybrid approach by combining both these techniques.

The supervised machine learning approach is based on the usage of the initial set of labeled documents/opinions, to determine the associated sentiment or opinion of any test set or new document. Among the different supervised learning techniques Support Vector Machine (SVM), Naive Bayes, Maximum Entropy, Artificial Neural Network (ANN), Random Forest, and Gradient Boosting are some of the most popular techniques which are employed in the sentiment analysis process. A brief introduction to each of these techniques is presented below; followed by a discussion on some of the research works using these algorithms either individually, in combination, or in comparison to each other.

SVM classifier is basically designed for binary classification. However, if the model is extended to support multi-class classification, One-vs-Rest (OvR)/One against all or One-vs-One (OvO)/One against one strategy is applied for the SVM classifier (Hsu and Lin 2002 ). In OvR, the multi-class dataset is re-designed into multiple binary datasets, where data belonging to one class is considered positive while the rest are considered negative. Using the binary datasets, the classifier is then trained. The final decision on the assignment of a class is made by choosing the class which classifies the test data with the greatest margin. Another, strategy One-vs-One (OvO) can also be used, and thus choose the class which is selected by majority classifiers. OvO involves splitting the original dataset into datasets representing one class versus every other class one by one.

Ahmad et al. ( 2018 ) presented a systematic review of sentiment analysis using SVM. Based on the papers published during the span of 5 years, i.e., from 2012 to 2017, they found that a lot of research works are published either using SVM directly for analysis or in a hybrid manner or even for comparing their proposed model with SVM. Some of the recent studies that used SVM for sentiment analysis are listed in Table ​ Table1 1 .

Recent literature on sentiment analysis using SVM

Authors	Title of the paper	Contribution(s)
Hidayat et al. ( )	Sentiment analysis of Twitter data related to Rinca Island development using Doc2Vec and SVM and logistic regression as a classifier.	Studied public opinion on Twitter regarding the development in Rinca Island using SVM and logistic regression. Used two types of Doc2Vec, distributed memory model of a paragraph vector (PV-DM) and a paragraph vector with a distributed bag of Words (PV-DBOW). The result of PV-DBOW with SVM, PV-DM with SVM showed the best results.
Cepeda and Jaiswal ( )	Sentiment Analysis on Covid-19 Vaccinations in Ireland using Support Vector Machine	Used tweets on the Covid-19 vaccination program in Ireland. A lexicon and rule-based VADER tool labeled the global dataset as negative, positive, and neutral. After that, Irish tweets were classified into different sentiments using SVM. Results show positive sentiment toward vaccines at the beginning of the vaccination drive, however, this sentiment gradually changed to negative in early 2021.
Mullen and Collier ( )	Sentiment analysis using support vector machines with diverse information sources	Uses SVMs to bring together diverse sources of potentially pertinent information, including several favourability measures for phrases and adjectives and, where available, knowledge of the topic of the text. Hybrid SVMs which combine unigram-style feature-based SVMs with those based on real-valued favourability measures obtain superior performance.
Zainuddin and Selamat ( )	Sentiment analysis using support vector machine	The features were extracted using N-grams and different weighting schemes. Use of Chi-Square weight features to select informative features for the classification using SVM proves to improve the accuracy.
Luo et al. ( )	Affective-feature-based sentiment analysis using SVM classifier	Considered text sentiment analysis as a binary classification. The feature selection method of Chi-square Difference between the Positive and Negative Categories (CDPNC) was proposed to consider the entire corpus contribution of features and each category contribution of features. The sentiment Vector Space Model (s-VSM) was used for text representation to solve data sparseness. With the combination of document frequency with Chi-Square, the experimental results were found to be superior to other feature selection methods using SVM.
Patil et al. ( )	Sentiment analysis using support vector machine.	Stated that SVM acknowledges some properties of text like high dimensional feature space, few irrelevant features, sparse instance vector and also eliminates the need for feature selection with the ability to generalize high dimensional feature space. The authors showed that the textual sentiment analysis performed better using SVM as compared to ANN.
Prastyo et al. ( )	Tweets Responding to the Indonesian Government’s Handling of COVID-19: Sentiment Analysis Using SVM with Normalized PolyKernel	The SVM analysis on the sentiments on general aspects using two-classes dataset achieved the highest performance in average accuracy, precision, recall, and f-measure. Demonstrated that the SVM algorithm with the Normalized Poly Kernel can be used to predict sentiment on Twitter for new data quickly and accurately.

There are basically two models which are commonly used for text analysis i.e., Multivariate Bernoulli Naive Bayes (MBNB) and Multinomial Naive Bayes (MNB) (Altheneyan and Menai 2014 ).

However, for continuous data, Gaussian Naive Bayes is also used. MBNB is used for classification when multiple keywords (features) represent a dataset. In MBNB, the document-term matrix is built using BoW, where the keywords for a document are represented by 1 and 0 based on the occurrence or non-occurrence in the document.

Whenever the count of occurrence is considered, MNB is used. In MNB, the distribution is associated with vector parameters θ c = ( θ c 1 , θ c 2 , . . . , θ ci ) for class c , where i is the number of keywords, and θ ci is the probability P ( V i ∣ C l a s s c ) of keyword V i appearing in a dataset belonging to class c . For estimating θ c , a smoothed variant of maximum likelihood namely relative frequency counting is employed as shown below.

where, α is the smoothing factor, N ci is the number of times keyword k appears in the training set and N c is the total number of keywords in class c .

To conduct a thorough investigation of the sentiment of micro-blog data, Le and Nguyen ( 2015 ) developed a sentiment analysis model using Naive Bayes and SVM, as well as information gain, unigram, bigram, and object-oriented feature extraction methods. Wawre and Deshmukh ( 2016 ) presented a system for sentiment classification that included comparisons of the common machine learning approaches Naive Bayes and SVM. Bhargav et al. ( 2019 ) used the Naive Bayes algorithm and NLP to analyze customer sentiments in various hotels.

Using the empirical probability distribution, maximum entropy models a given dataset by finding the highest entropy to satisfy the constraints of the prior knowledge. The unique distribution that shows maximum entropy is of the exponential form as shown in Eq.  12 .

Here, f i ( d o c i , C ) is a keyword and λ i is a parameter to be estimated. The denominator of Eq.  12 is a normalizing factor to ensure proper probability.

The flexibility offered by the maximum entropy classifier helps to augment syntactic, semantic, and pragmatic features with the stochastic rule systems. However, the computational resources and annotated training data required for the estimation of parameters for even the simplest maximum entropy model are very high. Thus, for large datasets, the model is not only expensive but is also sensitive to round-off errors because of the sparsely distributed features. For the estimation of parameters, different methods such as gradient ascent, conjugate gradient, variable metric methods, Generalized Iterative Scaling, and Improved Iterative Scaling are available (Hemalatha et al. 2013 ). Yan and Huang ( 2015 ) used the maximum entropy classifier to perform Tibetan sentences’ sentiment analysis, based on the probability difference between positive and negative outcomes. To identify the sentiment expressed by multilingual text, Boiy and Moens ( 2009 ) combined SVM, MNB, and maximum entropy describing different blogs, reviews, and forum texts using unigram feature vectors.

Deep learning (DL): Deep Learning is essentially an ANN with three or more layers that has the capability to handle large datasets and their associated complexities such as non-linearity, intricate patterns, etc. It involves the transformation and extraction of features automatically, which facilitates self-learning as it goes by multiple hidden layers, in a way similar to humans. These advantages of deep learning lead to enhanced performance of a sentiment analysis model and thus have led to its popularity since 2015 for the same. The input features of many deep learning models are generally preferred to be word embeddings. Word embeddings can be learned from text data by using an embedding layer, Word2Vec, or Glove vectors. Word2Vec can be learned either by the Continuous Bag of Words (CBOW) or the Continuous Skip-Gram model. Some of the common deep learning algorithms include CNNs, RecNN, RNN, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Deep Belief Networks (DBN). The detailed study by Yadav and Vishwakarma ( 2020 ) on sentiment analysis using DL, has found that LSTM performs better than other popular DL algorithms.

Tembhurne and Diwan ( 2021 ) provided valuable insight into the usage of several architectural versions of sequential deep neural networks, such as RNN, for sentiment analysis of inputs in any form, including textual, visual, and multimodal inputs. Tang et al. ( 2015 ) introduced several deep NNs with the use of sentiment-specific word embeddings for performing word-level, sentence-level, and lexical-level sentiment analysis. To encode the sentiment polarity of sentences, the authors introduced different NNs including a prediction model and a ranking model. They discovered discriminative features from different domains using sentiment embeddings to perform sentiment classification of reviews. According to the authors, the SEHyRank model shows the best performance among all the other proposed models. To fit CNN in aspect-based sentiment analysis, Wang et al. ( 2021 ) proposed an aspect mask to keep the important sentiment words and reduce the noisy ones. Their work made use of the position of aspects to perform aspect-based sentiment analysis in a unified framework. Hidayatullah et al. ( 2021 ) performed sentiment analysis using tweets on the Indonesian President Election 2019 using various deep neural network algorithms. According to the authors, Bidirectional LSTM (Bi-LSTM) showed better results as compared to CNN, LSTM, CNN-LSTM, GRU-LSTM, and other machine learning algorithms namely SVM, Logistic Regression (LR), and MNB. Soubraylu and Rajalakshmi ( 2021 ) proposed a hybrid convolutional bidirectional recurrent neural network, where the rich set of phrase-level features are extracted by the CNN layer and the chronological features are extracted by Bidirectional Gated Recurrent Unit (BGRU) through long-term dependency in a multi-layered sentence. Priyadarshini and Cotton ( 2021 ) suggested a sentiment analysis model using LSTM-CNN for a fully connected deep neural network and a grid search strategy for hyperparameter tuning optimization.

The Emotional Recurrent Unit (ERU) is an RNN, which contains a Generalized Neural Tensor Block (GNTB) and a Two-Channel Feature Extractor (TFE) designed to tackle conversational sentiment analysis. Generally, using ERU for sentiment analysis involves obtaining the context representation, incorporating the influence of the context information into an utterance, and extracting emotional features for classification. Li et al. ( 2022 ) employed ERU in a bidirectional manner to propose a Bidirectional Emotional Recurrent Unit (BiERU) to perform sentiment classification or regression. BiERU follows a two-step task instead of the three steps mentioned for simple ERUs. According to the source of context information, the authors presented two types of BiERUs namely, BiERU with global context (BiERU-gc) and BiERU with local context (BiERU-lc). As compared to c-LSTM (Poria et al. 2017 ), CMN (Hazarika et al. 2018 ), DialogueRNN (Majumder et al. 2019 ), and DialogueGCN (Ghosal et al. 2019 ), AGHMN (Jiao et al. 2020 ), BiERU showed better performance in most of the cases.

The low correlation between models is the key. Much the same as how speculations with low relationships meet up to shape a portfolio that is more prominent than the number of its parts, uncorrelated models can create group expectations that are more exact than any of the individual forecasts. The explanation behind this great impact is that the trees shield each other from their individual mistakes. While a few trees might not be right, numerous different trees will be correct, so as a gathering the trees can move the right way. So, the requirements for the random forest to perform well are:

There should be some real sign in our highlights so that models manufactured utilizing those highlights show improvement over random speculating.

The predictions made by the individual trees need to have low correlations with one another. As we realize that a forest is comprised of trees and more trees imply a more robust forest. Likewise, a random forest algorithm makes choice trees on information tests and afterward gets the forecast from every one of them, and lastly chooses the best arrangement by methods for casting a ballot. It is a gathering strategy that is superior to a solitary choice tree since it decreases the over-fitting by averaging the outcome.

Baid et al. ( 2017 ) analyzed the movie reviews using various techniques like Naïve Bayes, K-Nearest Neighbour, and Random Forest. The authors showed that Naïve Bayes performed better as compared to other algorithms. While performing sentiment analysis of real-time 2019 election twitter data, Hitesh et al. ( 2019 ) demonstrated that Word2Vec with Random Forest improves the accuracy of sentiment analysis significantly compared to traditional methods such as BoW and TF-IDF. This is because Word2Vec improves the quality of features by considering the contextual semantics of words.

Jain and Dandannavar ( 2016 ) suggested a system for sentiment analysis of tweets based on an NLP-based technique and machine learning algorithms such as MNB and decision tree, which use features extracted based on various parameters. For sentiment analysis of online movie reviews, Sharma and Dey ( 2012 ) have developed a noteworthy comparison of seven current machine learning techniques in conjunction with various feature selection approaches. Tan and Zhang ( 2008 ) also introduced a similar work, in which sentiment analysis of various fields, such as education, movies, and houses, is carried out using various feature selection methods along with machine learning techniques. Depending on the applicability and need for better-quality models for sentiment analysis, experts in the field use a variety of cascaded and ensemble approaches to combine machine learning algorithms with other existing options (Ji et al. 2015 ; Tripathy et al. 2015 ; Xia et al. 2011 ; Ye et al. 2009 ).

In unsupervised learning, the models are trained using unlabeled datasets. This technique in most cases relies on clustering methods such as k-means clustering, expectation-maximization, and cobweb. Darena et al. ( 2012 ) used k-means clustering through the use of Cluto 2.1.2 to determine the sentiment associated with customer reviews.

In self-supervised learning, the model begins with unlabeled datasets and then trains itself to learn a part of the input by leveraging the underlying structure of the data. Although the use of an unlabeled dataset gives this learning technique the notion of being unsupervised, they are basically designed to execute downstream tasks that are traditionally addressed by supervised learning. One of the self-supervised learning techniques which have gained a lot of popularity in recent years is the Pretrained Language Model (PML).

Typical steps in the process of creating a sentiment analysis model from scratch usually involve making use of standard sentiment lexicons, sentiment scoring and data labeling by human experts, and proper parameter tuning of the model that performs well on the rest of the dataset. This procedure could be expensive and time-consuming. PLM makes it simpler for developers of sentiment analysis models to implement the model in less training time with improved accuracy, by providing extensive semantic and syntactic information with the usage of a few lines of code. PLM acts as a reusable NLP model for various tasks associated with sentiment analysis such as PoS tagging, lemmatization, dependency parsing, tokenization, etc. Thus, PLMs can be proved to be advantageous to solve similar new tasks using old experience, without training the sentiment analysis model from the scratch.

Chan et al. ( 2022 ) provided a detailed study on the evolution and advancement of sentiment analysis using pretrained models. Additionally, the authors covered various tasks of sentiment analysis, for which the pretrained models can be used. The early works on PML involved transferring a single pretrained embedding layer to the task-oriented network architecture. To cope with numerous challenges such as word sense, polysemy, grammatical structure, semantics, and anaphora, models are presently being improved to a higher representation level.

Bidirectional Encoder Representations from Transformers BERT (Devlin et al. 2018 ), NLTK (Loper and Bird 2002 ), Stanford NLP (Manning et al. 2014 ), Universal Language Model Fine-tuning (ULMFit) (Howard and Ruder 2018 ), Embeddings from Language Models (ELMo) (Sarzynska-Wawer et al. 2021 ) are some of the well-known PLMs that serve as open-source NLP libraries for sentiment analysis. The performance of BERT was determined to be superior by Mathew and Bindu ( 2020 ) who thoroughly analyzed numerous PLMs that are frequently used for sentiment analysis.

Many pre-trained models use self-supervision strategies to learn the semantic content; however, give less importance to the sentiment-specific knowledge during the pre-training phase. There might also be a risk of overfitting associated with a pretraining model, which may lead to domain-specific sentiment mismatch between the source and the target domain. While dealing with social media related content, the PLM might cause biases in the results. The language in which the PLM was trained might differ from the language which is generally used in social media platforms. Further in-depth analysis and model development may be constrained if PLM behaves in a black-box manner. In a few cases, the PLM might not be able to handle the multi-class problem, if it was originally designed for identifying single or binary classes. This might also lead to ignorance/mishandling of one of the important classes, say neutral class, if the PLM was initially designed for handling positive and negative classes. Thus, while choosing a particular PLM model, we must consider the domain and data it was originally designed for. Also, a human expert might be required to validate the results, whenever required, to assure the quality of the sentiment analysis model.

Mao et al. ( 2022 ) provided an in-depth analysis of how PLMs are biased toward prompt-based sentiment analysis and emotion detection. According to the authors, the number of label classes, emotional label-word selections, prompt templates and positions, and the word forms of emotion lexicons leads to biased results. To address the issue of cross-domain tasks, Zhou et al. ( 2020 ) proposed SENTIX, a sentiment-aware model that learns the domain invariant sentiment knowledge during the pre-training phase. For addressing several factors related to sentiment analysis, experts have till now presented a variety of improvised modifications of the original PLMs. Some of them include Dynamic Re-weighting BERT (DR-BERT) (Zhang et al. 2022 ), BERT-based Dilated CNN (BERT-DCNN) (Jain et al. 2022 ), Attention-based ELMo (A-ELMo) (Huang and Zhao 2022 ), Contextual Sentiment Embeddings (CoSE) (Wang et al. 2022a ), Extended Universal Language Model Fine-Tuning (Ext-ULMFiT) and Fine-Tuned (FiT-BERT) (Fazlourrahman et al. 2022 ), etc.

Many researchers combine supervised and unsupervised techniques to generate hybrid approaches or even semi-supervised techniques which can be used to classify sentiments (König and Brill 2006 ; Kim and Lee 2014 ). With new information generated every millisecond, finding a fully labeled large dataset representing all the required information is nearly impossible. In such a scenario, semi-supervised algorithms train an initial model on a few labeled samples and then iteratively apply it to the greater number of unlabelled data and make predictions on new data. Among various semi-supervised techniques, Graph Convolution Network (GCN) (Kipf and Welling 2016 ; Keramatfar et al. 2022 ; Dai et al. 2022 ; Zhao et al. 2022 ; Lu et al. 2022 ; Yu and Zhang 2022 ; Ma et al. 2022 ) has recently gained the attention of researchers for performing sentiment analysis.

GCN is based on CNN which operates directly on graphs while taking advantage of the syntactic structure and word dependency relation to correctly analyze sentiment. GCNs learn the features by inspecting neighboring nodes. By using a syntactic dependency tree, a GCN model captures the relation among different words and links specific aspects to syntax-related words. Each layer of the multi-layer GCN architecture encodes and updates the representation of the graph’s nodes using features from those nodes’ closest neighbors. GCNs assist in performing node-level, edge-level, and graph-level prediction tasks for sentiment analysis, such as determining how connections on a social media platform affect the opinions of the users within that network, creating user recommendations based on connections between various products previously purchased, suggesting movies, etc. Generally, GCNs focus on learning the dependency information from contextual words to aspect words based on the dependency tree of the sentence. As a result, GCN has mainly attracted researchers in the field of aspect-based sentiment analysis.

Lu et al. ( 2021 ) built a GCN on the sentence dependency tree to fully utilize the syntactical and semantic information. Their methodology fixed the issues of incorrectly detecting irrelevant contextual words as clues for evaluating aspect sentiment, disregarding syntactical constraints, and long-range sentiment dependencies, which were present in earlier models. SenticGCN was proposed by Liang et al. ( 2022 ) to capture the affective dependencies of the sentences according to the specific aspects. To combine the affective knowledge between aspects and opinion words, the model performs aspect-based sentiment analysis using SenticNet along with GCN.

Along with the local structure information of a given sentence, such as locality, sequential knowledge, or syntactical dependency constraints within the sentence, global dependency information also holds importance in determining the sentiments accurately. Zhu et al. ( 2021 ) proposed a model named Global and Local Dependency Guided Graph Convolutional Networks (GL-GCN), where word global semantic dependency relations were revealed with the use of a word-document graph representing the entire corpus. An attention mechanism was adopted by the authors to combine both local and global dependency structure signals.

In general, the layers in GCN models are not devised distinctively for processing the aspect. To handle this issue, Chen et al. ( 2021 ) integrated GCN and co-attention networks for aspect-based sentiment analysis, to extract relevant information from contexts and remove the noise while considering colloquial texts. Tian et al. ( 2021 ) addressed the issues of the inability to learn from different layers of GCN, not considering dependency types, and lacking mechanisms for differentiating between various relations in the context of sentiment analysis. The authors utilized dependency types for aspect-based sentiment analysis with Type-aware GCN (T-GCN).

Opinion terms are used in a lexicon-based approach to execute sentiment classification tasks. This method suggests that a sentence’s or document’s cumulative polarity is the sum of the polarities of individual terms or phrases (Devika et al. 2016 ). According to Zhang et al. ( 2014 ), in opinion lexicon methods, evaluated and tagged sentiment-related words are counted and weighted with the use of a lexicon to perform sentiment analysis. This approach is based on sentiment lexicons, which are a compilation of recognized and pre-compiled terms, phrases, and idioms formed for traditional communication genres, according to Kharde and Sonawane ( 2016 ). More complex systems, such as dictionaries or ontologies, may also be used for this approach (Kontopoulos et al. 2013 ). Some of the recent lexicons available for sentiment analysis are discussed below in Table ​ Table2 2 .

Lexicons for sentiment analysis

S. no.	Sentiment lexica	Main feature	Pros and/or cons
1	Loughran and McDonald Sentiment Word Lists (Loughran and McDonald )	The dictionary reports counts, the proportion of the total, the average proportion per document, the standard deviation of proportion per document, document count, seven sentiment category identifiers, the number of syllables, and the source for each word. Indicator for sentiment related to financial context: “negative”, “positive”, “litigious”, “uncertainty”, “constraining”, or “superfluous”.	Cons: Does not contain acronyms, hyphenated words, names, or phrases, British English, Contains a limited number of abbreviations.
2	Stock Market Lexicon (Oliveira et al. )	Learning stock market lexicon from StockTwits for the stock market and general financial applications. About 17.44% of the StockTwits messages are labeled as “bullish” or “bearish” by their authors, to show their sentiment toward the mentioned stocks.	Pros: Presents Sentiment oriented word embeddings for the stock market. Cons: Imbalanced dataset Bullish is much higher than that labeled as Bearish, with an overall ratio of 4.03.
3	SentiWordNet 3.0 (Baccianella et al. )	Makes use of WordNet 3.0 to assign positive, negative, and objective scores to terms. Comprises more than 100,000 words that occur in different contexts.	Pros: For machine learning based sentiment classification a mixture of documents of different domains achieves good results. Cons: For Cross-domain sentiment analysis, rule-based approaches with fixed opinion lexica are unsuited.
4	SenticNet 7 (Cambria et al. )	The input sentence is translated from natural language into a sort of ‘protolanguage’ sentence, which generalizes words and multiword expressions in terms of primitives and, hence, connects these (in a semantic-role-labeling fashion) to their corresponding emotion and polarity labels.	Pros: Sentence, which generalizes words contains multiword expressions which enable polarity disambiguation. Cons: Sentence, which generalizes words do not handle sarcasm or antithetic opinion targets perfectly.
5	VADER (Hutto and Gilbert )	A lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media. Used for sentiment analysis of text which is sensitive to both the polarities, i.e., positive/negative and finds the intensity (strength) of emotion. Especially attuned to microblog-like contexts.	Pros: Not only presents the positivity and negativity score but also tells us about how positive or negative a sentiment is. Cons: May not work for complex data, does not recognize context, and requires additional tools for visualizing output.
6	Opinion Lexicon (Hu and Liu )	A list of positive and negative opinion words or sentiment words for English customer reviews (around 6800 words).	Cons: Does not help to find features that are liked by customers.
7	MPQA Subjectivity Lexicon (Wilson and Wiebe )	In the corpus, individual expressions are marked that correspond to explicit mentions of private states, speech events, and expressive subjective elements. Annotators were asked to judge all expressions in context. Includes 5,097 negative and 2,533 positive words. Each word is assigned a strong or weak polarity.	Cons: It is rooted in the subjective interpretations of a single person.
		Works great for short sentences, such as tweets or Facebook posts.
8	NRC Hashtag Sentiment Lexicon (Mohammad and Kiritchenko ; Mohammad )	Association of words with positive (negative) sentiment generated automatically from tweets with sentiment-word hashtags such as #amazing and #terrible. Number of terms: 54,129 unigrams, 316,531 bigrams, 308,808 pairs, Association scores: real-valued.	Words can have multiple meanings and senses, and the meaning and sense that is common in one domain may not be common in another. Furthermore, words that are not generally considered sentiment-bearing can imply sentiments in specific contexts.
9	NRC Hashtag Emotion Lexicon (Mohammad et al. ; Zhu et al. ; Kiritchenko et al. )	Association of words with eight emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust) and two sentiments (negative and positive). Manually annotated on Amazon’s Mechanical Turk. Generated automatically from tweets with emotion-word hashtags such as #happy and #anger. Number of terms: 16,862 unigrams (words), 5,000 word senses, Association scores: real-valued	Pros: Available in 40 different languages. Cons: Words can have multiple meanings and senses, and the meaning and sense that is common in one domain may not be common in another. Furthermore, words that are not generally considered sentiment-bearing can imply sentiments in specific contexts.
10	NRC Hashtag Affirmative Context Sentiment Lexicon (Mohammad et al. ; Zhu et al. ; Kiritchenko et al. )	Association of words with positive (negative) sentiment in affirmative or negated contexts generated automatically from tweets with sentiment-word hashtags such as #amazing and #terrible. Number of terms: Affirmative contexts: 36,357 unigrams, 159,479 bigrams. Association scores: real-valued	Pros: Available in 40 different languages.
			Cons: Words can have multiple meanings and senses, and the meaning and sense that is common in one domain may not be common in another. Furthermore, words that are not generally considered sentiment-bearing can imply sentiments in specific contexts.
11	NRC Hashtag Negated Context Sentiment Lexicon (Mohammad et al. ; Zhu et al. ; Kiritchenko et al. )	Association of words with positive (negative) sentiment in negated contexts generated automatically from tweets with sentiment-word hashtags such as #amazing and #terrible. Number of terms: Negated contexts: 7,592 unigrams, 23,875 bigrams. Association scores: real-valued	Pros: Available in 40 different languages. Cons: Words can have multiple meanings and senses, and the meaning and sense that is common in one domain may not be common in another. Furthermore, words that are not generally considered sentiment-bearing can imply sentiments in specific contexts.
12	NRC Word-Emotion Association Lexicon/NRC Emotion Lexicon (Mohammad and Turney , )	Association of words with eight emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust) and two sentiments (negative and positive) manually annotated on Amazon’s Mechanical Turk. Available in 40 different languages. Number of terms: 14,182 unigrams (words), 25,000 word senses. Association scores: binary (associated or not).	Pros: Available in 40 different languages. Cons: Words can have multiple meanings and senses, and the meaning and sense that is common in one domain may not be common in another. Furthermore, words that are not generally considered sentiment-bearing can imply sentiments in specific contexts.
13	Emoticon Lexicon/Sentiment140 Lexicon (Mohammad et al. ; Zhu et al. ; Kiritchenko et al. )	Association of words with positive (negative) sentiment generated automatically from tweets with emoticons such as:) and:(. Number of terms: 62,468 unigrams, 677, 698 bigrams, 480,010 pairs. Number of terms: 14,182 unigrams (words), 25,000 word senses. Association scores: real-valued.	Pros: Available in 40 different languages. Cons: Words can have multiple meanings and senses, and the meaning and sense that is common in one domain may not be common in another. Furthermore, words that are not generally considered sentiment-bearing can imply sentiments in specific contexts.
14	Sentiment140 Affirmative Context Lexicon (Mohammad et al. ; Zhu et al. ; Kiritchenko et al. )	Association of words with positive (negative) sentiment in affirmative contexts generated automatically from tweets with emoticons such as:) and:(. Number of terms: Affirmative contexts: 45,255 unigrams, 240,076 bigrams.	Pros: Available in 40 different languages. Cons: Words can have multiple meanings and senses, and the meaning and sense that is common in one domain may not be common in another. Furthermore, words that are not generally considered sentiment-bearing can imply sentiments in specific contexts.
15	Yelp Restaurant Sentiment Lexicon (Kiritchenko et al. )	The Yelp dataset is a subset of our businesses, reviews, and user data for use in personal, educational, and academic purposes. Created from the Yelp dataset, from the subset of entries about these restaurant-related businesses.	Cons: Few reviews are considered to be fake. No proper boundary to detect neutrality.
		Consists of 10 attributes, namely, unique Business ID, Date of Review, Review ID, Stars given by the user, Review given by the user, Type of text entered—Review, Unique User ID, Cool column: The number of cool votes the review received, Useful column: The number of useful votes the review received, Funny Column: The number of funny votes the review received. Number of reviews: 183,935 reviews. Have one to five-star ratings associated with each review.
16	Amazon Laptop Sentiment Lexicon (McAuley and Leskovec )	Collected reviews posted on Amazon.com from June 1995 to March 2013. Extracted from this subset are all reviews that mention either a laptop or notebook. Have one to five-star ratings associated with each review. 26,577 entries for unigrams (includes affirmative and negated context entries), 155,167 entries for bigrams.	Cons: May not work well with the neutral sentiment.
17	Macquarie Semantic Orientation Lexicon (Mohammad et al. )	76,400 terms. Sentiments: negative, positive Automatic: Using the structure of a thesaurus and affixes.	–
18	Harvard’s General Inquirer Lexicon (Stone and Hunt )	A lexicon attaching syntactic, semantic, and pragmatic information to part-of-speech tagged words. 2000 positive and 2000 negative words.	–
19	IMDB (Yenter and Verma )	50K movie reviews. A set of 25,000 highly polar movie reviews for training and 25,000 for testing.	Pros: The data is refreshed daily. Cons: IMDB reviews are not considered to be overly trustworthy, as big Hollywood studios generally dictate the scores and the overall consensus. The algorithm used by IMDB to collate its reviews is generally considered inferior to those used by Rotten Tomatoes and similar sites.
20	AFINN (Nielsen )	AFINN is the simplest yet most popular lexicon used for sentiment analysis developed by Finn Årup Nielsen. It contains 3300+ words with a polarity score associated with each word. A list of English terms manually rated for valence with an integer between -5 (negative) and +5 (positive) by Finn Årup Nielsen between 2009 and 2011. Primarily analyze Twitter sentiment.	Cons: Using the raw AFINN score the longer texts may yield higher values simply because they contain more words.
21	Corpus of Business News (Moreno-Ortiz et al. )	Covers non-specific sentiment-carrying terms and phrases. It contains 6,470 entries, both single and multi-word expressions, each with tags denoting their semantic orientation and intensity.	Pros: A wide coverage, a domain-specific lexicon for the analysis of economic and financial texts in English.
22	DepecheMood Affective Lexicon (Staiano and Guerini )	Harvested crowd-sourced affective annotation from a social news network. Considered the affective dimensions namely Afraid, Amused, Angry, Annoyed, Don’_Care, Happy, Inspired, and Sad. 37 thousand terms annotated with emotion scores.	Cons: Cannot handle similar words which are not present in the training document.
23	Financial Phrasebank (Malo et al. )	Polar sentiment dataset of sentences from financial news. The dataset consists of 4840 sentences from English-language financial news categorized by sentiment. The dataset is divided by an agreement rate of 5–8 annotators.	Pros: Works well for NLP-related tasks in multi-class financial domain classifications.

The lexicon-based approach is categorized into three methods: manual, dictionary-based, and corpus-based methods based on the various approaches to classification (Zhang et al. 2014 ). Because of the considerable time investment, researchers seldom use the manual approach, though it is often paired with the other two automated approaches.

Dictionary-based approach starts with a series of manually annotated opinion seed terms. The collection is then extended by searching through a dictionary such as WordNet (Miller et al. 1990 ) to find synonyms and antonyms. SWN (Baccianella et al. 2010 ) is one of the earliest thesauri and makes use of WordNet to assign positive, negative, and objective ratings to terms. The new words are added to the initial list after they have been discovered. The next iteration begins and the method continues until no new words need to be added after a particular point. While considering valence shifters (intensifiers, downtoners, negation, and irrealis markers), Read and Carroll ( 2009 ) proposed a word-level sentiment analysis model called Semantic Orientation CALculator (SO-CAL). In SO-CAL, lexicon-based sentiment classification is performed using dictionaries of sentiment-bearing terms annotated with their polarities and strengths.

The use of a dictionary for sentiment analysis suffers from one major drawback. This methodology does not adequately handle the domain and context-sensitive orientations of opinion terms.

The corpus-based approach uses syntactic patterns or co-occurring patterns in a vast corpus to extend the original seed list of opinion terms (Cambria and Hussain 2015 ). It is very tough to generate a huge corpus using the corpus-based approach, to cover each and every English word. However, using a domain corpus has the advantage of allowing you to identify the domain and context-related opinion terms as well as their orientations. The corpus-based approach aims to provide dictionaries that are specially related to a particular domain (Kharde and Sonawane 2016 ). To expand the dictionary, statistical or semantic approaches may be used to look for words that are similar as discussed below.

The statistical approach includes searching co-occurrence patterns or seed opinion words. Searching for co-occurrence trends or seed opinion terms is one statistical technique. If the corpus is insufficient, the issue of certain words not being available can be solved by using the whole collection of indexed documents on the web as the corpus for creating the dictionary (Turney 2002 ). In a broad annotated corpus, even the appearance of a word in the positive or negative text may be used to determine its polarity (Read and Carroll 2009 ). Similar opinion words are likely to co-occur in a corpus, according to Cambria and Hussain ( 2015 ), and hence the polarity of an unfamiliar word can be calculated using the relative frequency of co-occurrence with another word. In this case, PMI can be used (Turney 2002 ). The statistical approach to the semantic orientation of a word is used in conjunction with PMI (Cambria and Hussain 2015 ). Another such approach is Latent Semantic Analysis (LSA) (Deerwester et al. 1990 ).

Semantically close words are assigned similar polarities based on this approach. This method is based on various criteria for measuring word similarity (Cambria and Hussain 2015 ). The relative count of positive and negative synonyms of an unknown word can be used to find out the polarity of that word using different semantic relationships given by WordNet (Kim and Hovy 2004 ).

A combination of both statistical and semantic approaches is also followed by a few researchers to perform sentiment analysis. Zhang et al. ( 2012 ) applied a mixture of both these approaches to online reviews to determine the weakness of products. Sentence-based sentiment analysis, according to their model, is carried out by taking into account the effect of degree adverbs to determine the polarity of each aspect within a sentence. To find the implicit features, they used the collocation statistics-based selection method-Pointwise Mutual Information (PMI). With the use of semantic methods, feature words of the products are grouped into corresponding aspects.

Ding et al. ( 2008 ) demonstrated that the same term can have multiple polarities in different contexts, even within the same domain. Therefore, rather than simply finding domain-dependent sentient words using the corpus-based approach, they explored the notion of intra-sentential and inter-sentential sentiment consistency.

In the lexicon-based approach, one point is worth noticing. The initial manual annotation of the seed list can be a costly procedure. Secondly and most importantly, the use of a dictionary even for seed list generation can lead to the insufficiency of handling cross-domain problems. Thus, the usage of a proper technique to generate a seed list for a lexicon-based approach is an open problem. Also, whenever linguistic rules are involved in handling knowledge, there might be situations where it fails to correctly grasp the affective sentiment.

Hybrid approaches which use sentiment lexicons in machine learning methods have also attracted many researchers to combine the benefits of both approaches. Trinh et al. ( 2018 ) used the hybrid approach to perform sentiment analysis of Facebook comments in the Vietnamese language. While their dictionary is partly based on SO-CAL, the authors manually built the dictionary to include nouns, verbs, adjectives, and adverbs along with emotional icons. They performed sentence-level sentiment analysis of product reviews using the SVM classifier. Appel et al. ( 2016 ) also performed sentence-level sentiment analysis using a combination of lexicon and machine learning approaches. They extended their sentiment lexicon with the use of SWN and used fuzzy sets to determine the polarity of sentences. Using an SVM classifier, Zhang et al. ( 2011 ) performed entity-level sentiment analysis of tweets, with the use of a lexicon that supports business marketing or social studies. They made use of the lexicon by Ding et al. ( 2008 ) along with some frequently used opinion hashtags to build the lexicon for their model. Pitogo and Ramos ( 2020 ) performed sentiment analysis for Facebook comments using a lexicon-based approach called Valence Aware Dictionary and Sentiment Reasoner (VADER) along with a hierarchical clustering algorithm.

Sentiment or opinion summarization

Sentiment or Opinion summarization or aggregation aims to provide an idea of the overall influence or polarity depicted by the dataset, by summing up the polarity of all individual words/aspects /sentences/documents of the dataset. Sentiment summarization must not be confused with text summarization, though they are slightly related. Text summarization aims to provide a summary of the dataset, while sentiment summarization provides a generalized polarity depicted by the whole dataset.

Different types of summarization models are proposed by researchers to obtain an average sentiment. Pang and Lee ( 2004 ) first extracted all subjective sentences and then summarized those subjective sentences. Blair-Goldensohn et al. ( 2008 ) used a tool to choose a few representative documents from a vast number of documents and then used them for emotion summarization based on aspects. By mining opinion features from product feedback, Hu and Liu ( 2004 ) suggested an aspect-based sentiment summarization strategy for online consumer reviews. Using the ratings on different aspects, Titov and McDonald ( 2008 ) proposed a model which can contribute to the sentiment summarization process. Their algorithm is designed to find related topics in text and collect textual evidence from reviews to support aspect ratings. Sokolova and Lapalme ( 2009 ) developed an emotion summarization model to summarise the opinionated text in consumer goods by integrating different polarity detection techniques and automated aspect detection algorithms. Different types of summarization models are proposed by researchers to obtain an average sentiment. Pang and Lee ( 2004 ) first extracted all subjective sentences and then summarized those subjective sentences. Blair-Goldensohn et al. ( 2008 ) used a tool to choose a few representative documents from a vast number of documents and then used them for emotion summarization based on aspects. By mining opinion features from product feedback, Hu and Liu ( 2004 ) suggested an aspect-based sentiment summarization strategy for online consumer reviews. Using the ratings on different aspects, Titov and McDonald ( 2008 ) proposed a model which can contribute to the sentiment summarization process. Their algorithm is designed to find related topics in text and collect textual evidence from reviews to support aspect ratings. Bahrainian and Dengel ( 2013 ) developed an emotion summarization model to summarise the opinionated text in consumer goods by integrating different polarity detection techniques and automated aspect detection algorithms.

Performance analysis measures

The evaluation of performance is one of the principal concepts associated with building a resourceful model. Once the sentiments are classified as either positive or negative, the performance of the model needs to be evaluated. The papers by Sokolova and Lapalme ( 2009 ) provided a better understanding of the applicability of performance measures depending on the variability of the classification tasks. Among different kinds of available metrics for measuring the performance of a textual sentiment analysis model, metrics based on the confusion matrix are widely used (Sokolova and Lapalme 2007 , 2009 ; John and Kartheeban 2019 ). The details concerning the classifications that are expected and those that are calculated by a classifier are shown in the confusion matrix. A confusion matrix for binary classification problems consists of four separate data entries, namely True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN), as shown in Table ​ Table3 3 .

Confusion matrix for binary classification

		Actual classification
		Positive	Negative
Expectation	Positive	TP	FN
	Negative	FP	TN

TP overall positive data that are classified as positive, TN overall negative data that are classified as negative, FP overall negative data that are classified as positive, FN overall positive data that are classified as negative

The most frequently used performance metric is accuracy to measure the overall effectiveness of the model. Accuracy determines the proportion of a total number of instances (i.e., documents/ sentences/words) that are correctly predicted by the sentiment analysis model. Equation  13 shows the formula for estimating the model’s accuracy.

Apart from accuracy, precision and recall are well-known metrics that are best suited for text applications (Sokolova and Lapalme 2007 ). The number of correctly classified positive instances is determined by positive predictive value or precision, while the number of correctly classified negative instances is determined by negative predictive value. The proportion of actual positive instances that are correctly classified is determined by sensitivity or recall; the proportion of actual negative instances that are correctly classified is determined by negative recall or specificity.

The following are the formulas for calculating them (Salari et al. 2014 ).

Precision and recall are better indicators of the current system’s success than accuracy for an imbalanced binary classifier. Yet, in certain situations, a system may have high precision but poor recall, or vice versa. In this case, the f-measure allows you to articulate all issues with a single number. Once the precision and recall for a binary or multi-class classification task have been calculated, the two scores together form the f-measure, as seen in Eq.  18 . F - m e a s u r e , F = 2 ∗ P r e c i s i o n ∗ R e c a l l P r e c i s i o n + R e c a l l 18 Accuracy or f-measure can show overoptimistic inflated results, especially on imbalanced datasets. Matthew’s Correlation Coefficient (MCC) is a more reliable statistical rate that produces a high score only if the prediction obtained good results in all of the four confusion matrix categories proportionally, both to the size of positive elements and the size of negative elements in the dataset. The confusion matrix or an error matrix can be summed up using MCC as shown in Eq.  19

. MCC ranges from [− 1,1], where 1 indicates the best agreement between the predicted and actual values. The MCC helps us to identify the ineffectiveness of the classifier in classifying especially the minority class samples. M C C = T N ∗ T P - F N ∗ F P ( T P + F P ) ( T P + F N ) ( T N + F P ) ( T N + F N ) 19 To measure the ability of a sentiment classifier to distinguish between the polarity classes, an Area Under the Curve (AUC) is employed. The curve in AUC is generally a ROC (Receiver Operating Characteristic) curve, which is a graph showing the performance of a classification model at all classification thresholds as shown in Fig. 

3 . ROC plots TP and FP. AUC is an aggregated evaluation of the classifier as the threshold varies over all possible values. The Precision-Recall AUC summarizes the curve using a range of threshold values as a single score. AUC measures how true positive rate (recall) and false positive rate trade-off. Specifically, for imbalanced datasets, where overfitting needs to be avoided, AUC works as a preferable evaluation matrix. AUC represents the probability that a random positive instance is positioned to the right of a random negative instance. AUC ranges from 0 to 1. An AUC of 0.0 denotes a model that makes all incorrect classifications, whereas an AUC of 1.0 denotes a model that makes all correct classifications.

AUC under ROC

When a regression task is adopted for sentiment analysis, Mean Squared Error (MSE) is employed to find the squared difference between actual and predicted values. It is an absolute measure of the goodness of fit of dependent variables in the model. The formula of MSE is given in Eq.  20 . The lower the value of MSE, the better the sentiment analyzer. It can be used as a loss function as the graph of MSE is differentiable. However, it is not very suitable in case the dataset contains outliers.

In contrast to the context dependency of MSE, R squared is a context-independent metric that is used for a regression task. It is a relative measure of how well the model fits dependent variables or how close the data is to the fitted regression line. Coefficient of Determination and Goodness of Fit are other names for R squared and it is calculated using Eq.  21 .

where SSR is the squared sum error of the regression line and SSM is the squared sum error of the mean line.

Other performance evaluation metrics that can be also considered for evaluating a sentiment analysis model are Root Mean Squared Error (RMSE), Residual Standard Error (RSE), Mean Absolute Error (MAE), etc.

Applications of sentiment analysis

Sentiment analysis or opinion mining has recently been used in studies on e-commerce feedback, tweets, Facebook posts, YouTube content, blog entries, and a variety of other data mining and knowledge-based AI programs. As a result, it has progressed significantly in fields including Information Retrieval (IR), web data analysis, mining of text, analysis of text, NLP, computational linguistics, and biometrics. Using different approaches/methods/ frameworks analyzed in this paper beforehand, sentiment analysis can be applied to various fields such as tourism, education, defense, business, politics, public, finance, hazards, health, and safety. The broad range of applications will aim to obtain the best possible combination of strengths, whether or not any of the components in Fig.  1 or any of the approaches indicated in Fig.  2 are present. Depending on the requirement/aim/ framework of a sentiment analysis model, applications can vary from a straightforward prediction of the polarity of a single word to uncovering sensitive or hidden information, or even a pattern to protect a nation from any potential terrorist attack or disaster. Many research works mention different application areas based on different domains or approaches used (Alessia et al. 2015 ; Jain and Gupta 2022 ; Saxena et al. 2022 ; Feldman 2013 ; Govindarajan 2022 ; Ravi and Ravi 2015 ). The knowledge of diverse application fields based purely on the dataset at hand is challenging to find in existing research papers. This paper aims to outline several sentiment analysis application areas based on the data/content/material in hand, that can be used by researchers for sentiment analysis.

Reviews on products

Sentiment analysis using reviews on different products with different brands is the most widespread practice, which encompasses different application angles. For a particular product, the number of brands has been increasing day to day. Also, the same brand may offer products with different specifications. Nowadays even different online shopping sites are available that sell the same product. This creates confusion among customers to reach an optimal decision. Though shopping sites offer the option of displaying comments and star ratings left by former customers to assist potential buyers, the count of current feedback can be so large that scrolling through thousands of them can be a time-consuming process. Sentiment analysis helps to alleviate this condition by giving a concise perspective on a product or brand as a whole, or even on a certain feature/aspect of the product. Also, it can be used by the sellers or manufacturers to concentrate on the suitable aspects or specifications, which can be used for upgrading the product or deciding the advertisement strategy. Product analysis by buyers, suppliers, and sellers; competitor analysis or market study by sellers or manufacturers; brand tracking and reputation management by manufacturers; customer service by e-commerce sites; and customer analysis by sellers and manufacturers are among the various application directions associated with sentiment analysis of product feedback. The necessity to detect fake reviews before using the available data for decision-making was highlighted in the research work by Vidanagama et al. ( 2022 ). The authors made use of a rule-based classifier, a domain feature ontology, and Mahalanobis distance to detect fake reviews while performing aspect-based sentiment analysis. Cao et al. ( 2022 ) have introduced a quality evaluation model of products by combining deep learning, word vector conversion, keyword clustering, and feature word extraction technologies. Their model improves product features based on consumer online reviews and finally calculates customer satisfaction and attention based on short text comments with sentiment tags. With the use of pre-trained word embeddings, Bhuvaneshwari et al. ( 2022 ) proposed a Bi-LSTM Self Attention based CNN (BAC) model for analysis of user reviews. Wang et al. ( 2022b ) designed multi-attention bi-directional LSTM (BLSTM(MA)), and used Latent Dirichlet Allocation (LDA) modeling to perform multimodal fusion for sentiment analysis of product reviews. Alantari et al. ( 2022 ) examined 260,489 reviews from five review platforms, covering 25,241 products in nine different product categories. They discovered that pretrained neural network-based machine learning techniques, in particular, provide the most precise forecasts, while topic models like LDA provide more thorough diagnostics. To make predictions, topic models are better suited than neural network models, which are not good at making diagnoses. As a result, the preference of the analysts for prediction or diagnostics is likely to determine how text review processing technologies are chosen in the future.

Political Tweets, Facebook comments, Blog posts, and YouTube Videos

Recently, people have started to openly share their views or opinion on different political parties, electoral candidates, government policies, and rules on different public platforms such as Twitter, Facebook, YouTube, and blogs. These create a great influence on the followers. Therefore, they are used by many experts to predict the outcome of an election beforehand, monitor public sentiment on various political movements, or analyze the sentiment of the public on a proposed government rule, bill, or law.

With the use of pre-trained models and the Chi-square test, Antypas et al. ( 2022 ) proposed a multilingual sentiment analysis model to analyze both influential and less popular politicians’ tweets from members of parliament of Greece, Spain, and the United Kingdom. Their study indicates that negative tweets spread rapidly as compared to positive ones. Using Valence Aware Dictionary and sentiment Reasoner (VADER), and 2 million tweets on the 2019 Indian Lok Sabha Election, Passi and Motisariya ( 2022 ) analyzed sentiments of Twitter users towards each of the Indian political parties. Using the aging estimation method with the proportion of positive message rate to negative messages rate, Yavari et al. ( 2022 ) designed an indicator of the election results in the future.

Tweets or comments on Facebook/YouTube/Instagram on social cause or events

Expressions of opinions on different social causes or events have also increased recently. This increases the scope of designing application portals that perform public sentiment analysis, monitor, and predict different possible outcomes of such an event or cause and decide the possible steps which need to be adopted in the future in case there is an outbreak of any chaotic situation.

A multi-grained sentiment analysis and event summary method employing crowd-sourced social media data on explosive accidents was built by Ouyang et al. ( 2017 ). The system can determine which components of the event draw users’ attention, identify which microblog is responsible for a large shift in sentiment, and detect those aspects of the event that affect users’ attention. Smith and Cipolli ( 2022 ) studied the emotional discourse before and after a prohibition on graphic photos of self-harm on Facebook and Instagram using a corpus of 8,013 tweets. By clarifying topical content using statistical modeling to extract abstract topics in discourse, the authors offered an insight into how the policy change relating to self-harm was viewed by those with a vested interest.

Reviews on Blogs/Tweets/Facebook comments on movie

Reviews on an upcoming movie or a movie that is onscreen in the theatres can be used to decide the success or failure of the movie. Different movie recommender systems can also be designed using the reviews from the audience. Also, the distributors or producers can use such reviews to improve their advertising strategy based on the different aspects which are liked by the viewers.

Using sentiment analysis to gain a deeper understanding of user preferences, Dang et al. ( 2021 ) proposed methods to enhance the functionality of recommender systems for streaming services. The Multimodal Album Reviews Dataset (MARD) and Amazon Movie Reviews were used to test and compare two different LSTM and CNN combinations, LSTM-CNN and CNN-LSTM. They started with a version of the recommendation engine without sentiment analysis or genres as their baseline. As compared to the baseline, the results demonstrate that their models are superior in terms of rating prediction and top recommendation list evaluation. Pavitha et al. ( 2022 ) designed a system for analyzing movie reviews in different languages, classifying them into either positive or negative using Naive Bayes and Support Vector Classifier (SVC), and recommending similar movies to users based on Cosine Similarity. For B-T4SA and IMDB movie reviews, Zhu et al. ( 2022 ) proposed a self-supervised sentiment analysis model namely Senti-ITEM. The model pairs a representative image with the social media text as a pretext task, extract features in a shared embedding space, and uses SVM for sentiment classification.

Tweets/Facebook comments on pandemic/crisis /environmental issues

Nowadays people encountering abrupt situations or difficulties due to the Covid-19 pandemic or any environmental issues such as storm or earthquake posts real-time tweets or comments on Facebook. In such a situation, by analyzing tweets or comments properly, government or any agency, or even nearby people can offer help, and perform disaster management and crisis analysis.

Hodson et al. ( 2022 ) suggested a corpus-assisted discourse analysis approach, for analyzing public opinion on COVID-19 tweets and YouTube comments related to Canadian Public Health Office. The authors found that different platforms convey key differences between comments, specifically based on the tone used in YouTube videos as compared to plain text in Tweets. To capture sarcasm or get clear information, cross-platform and diverse methods must be adopted to facilitate health-related communication and public opinion. Chopra et al. ( 2022 ) employed logistic regression, Naive Bayes, XGBoost, LSTM, GloVe, and BERT to predict disaster warnings from tweets and evaluate the seriousness of the content.

Tweets/Facebook comments/YouTube videos on Stock Market

One of the trending application areas of sentiment analysis is Stock Market Prediction. Identifying stocks and share with great potential and deciding the optimal time to buy them at the lowest price and sell them at the peak time can be performed using a suitable sentiment analysis model. Using stock market data with SVM, Ren et al. ( 2018 ) suggested a model that forecasts movement direction and predicts stock prices while capturing investor psychology. Sousa et al. ( 2019 ) used the BERT algorithm to analyze the sentiments of news articles and provide relevant information that can facilitate stock market-related quick decision-making. Considering both positive and negative financial news, de Oliveira Carosia et al. ( 2021 ) analyzed the influence on the stock market using three Artificial Deep Neural Networks namely Multi-Layer Perceptron (MLP), LSTM, and CNN. The findings of this sentiment analysis model’s observations revealed that while recurrent neural networks, such as LSTM, perform better in terms of time characteristics when used to predict the stock market, CNNs perform better when assessing text semantics.

Future scope of research in sentiment analysis

There are numerous scientific studies in the literature that focus on each of the components of the sentiment analysis approach, either independently or in combination. Each of these sentiment analysis modules offers plenty of opportunities for further investigation, improvisation, and innovation. Several challenges and issues are also faced during the process of performing sentiment analysis, which may hinder the proper functioning or performance of the model. Some of them are domain dependency, reference problems, sarcasm detection, spam detection, time period, etc. Most of these challenges influence the development of better techniques and algorithms to handle them. Some of the primary research gaps that offer scope for future research and hence encourage further sentiment analysis research are discussed below:

• It has been found that current techniques dedicated to sentiment analysis do not employ effective data initialization and pre-processing techniques. Rather than relying on established NLP pre-processing techniques, an advanced pre-processing technique, such as standard normalization that takes deliberately into account, the case of negation and mixed emotion would be extremely beneficial.
• One of the most critical steps in improving the performance of a sentiment analysis model is keyword extraction. Many sentiment analysis models have been observed to extract keywords using generalized dictionaries. The use of generalized dictionaries, on the other hand, produces inaccurate findings since most of these dictionaries include keywords that are relevant to a specific domain. However, there is no predefined list of keywords for a certain domain or subject in the real world. Different researchers have shown the supremacy of the degree centrality metric for the graph-based method of obtaining the best collection of representative and sentimental words. As a result, it may be used to find key terms or phrases. Automatic keyword extraction techniques can be used for sentiment analysis in a variety of applications, both independently and in combination. Most of these techniques have found applications in a variety of research areas, including Data Analysis, TM, IR, and NLP since they allow for the condensing of text records.
• Assignment of polarity scores to keywords using sentiment dictionaries has gained a lot of attention in sentiment analysis. However, depending on its use in a specific domain, a term can serve as a positive or negative word at different times. Therefore, the usage of sentiment dictionaries with pre-defined polarities for words is not an appropriate practice for sentiment analysis. Existing sentiment dictionaries fail to handle sarcasm or negations to a great extent. It is observed that many machine learning based techniques are trained to work only for a particular domain. They do not consider that the words can change their polarity based on the context and domain of application. Thus, whenever the same word is tested for another domain using the trained classifier, it shows incorrect results in some situations.
• New edge and node weighing approaches may be introduced and used in place of NE-Rank or TextRank centralities to determine keyword rank. To achieve improved outcomes in the future, different ensemble or individual improvised centralities may be used. This establishes a framework for future research into graph mining algorithms for sentiment analysis in various fields.

The era of digitization marks the astonishing growth of subjective textual data online. Proper analysis of the textual information, to rightly reflect the public sentiment regarding any topic, demands proper investigation of textual data. Sentiment analysis has emerged as the most important task which helps to enhance the decision-making process by extracting the underlying sentiment or opinion of data. Even though sentiment analysis has progressed in recent years, modern models have flaws such as domain dependence, negation management, high dimensionality, and the failure to use efficient keyword extraction. This paper examines and provides a comprehensive discussion of different perspectives related to the creation and implementation of an effective sentiment analysis model. A thorough examination and establishment of various modules of the sentiment analysis methodology are carried out to plan and improve effective sentiment analysis models. The keyword extraction algorithm is vital to the success of a sentiment analysis model and thus is well-studied in this paper. The paper also discusses sentiment classification methods, which form an essential aspect of a sentiment analysis model. The paper conducts a detailed review of both machine learning and lexicon-based approaches to textual data sentiment analysis.

As a thorough, well-organized study on sentiment analysis, this research effort can assist academicians and industry experts in analyzing and developing powerful sentiment analysis models in a wide range of domains. Sentiment analysis models have a lot of potential for further development and use in the near future because they have a broad range of uses in social, industrial, political, economic, health and safety, education, defense financial contexts, and others. Each of the sentiment analysis modules as discussed in this paper can be investigated, improvised, and supplemented with certain relevant algorithms to design an efficient sentiment analysis model. This study also offers prospective guidelines for carrying out proper sentiment analysis research.

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Monali Bordoloi, Email: [email protected] .

Saroj Kumar Biswas, Email: moc.oohay@mukjorassib .

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It's the Sentiment that Counts: Comparing Sentiment Analysis Tools for Estimating Affective Valence in Dream Reports

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• 1 Department of Psychology, University of Kansas, Lawrence, Kansas, USA.
• 2 Independent Researcher, Lawrence, Kansas, USA.
• PMID: 39252583
• DOI: 10.1093/sleep/zsae210

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Sentiment analysis dataset in Moroccan dialect: bridging the gap between Arabic and Latin scripted dialect

• Original Paper
• Published: 11 September 2024

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• Mouad Jbel 1 ,
• Mourad Jabrane 1 ,
• Imad Hafidi 1 &
• Abdulmutallib Metrane 1  

Sentiment analysis, the automated process of determining emotions or opinions expressed in text, has seen extensive exploration in the field of natural language processing. However, one aspect that has remained underrepresented is the sentiment analysis of the Moroccan dialect, which boasts a unique linguistic landscape and the coexistence of multiple scripts. Previous works in sentiment analysis primarily targeted dialects employing Arabic script. While these efforts provided valuable insights, they may not fully capture the complexity of Moroccan web content, which features a blend of Arabic and Latin script. As a result, our study emphasizes the importance of extending sentiment analysis to encompass the entire spectrum of Moroccan linguistic diversity. Central to our research is the creation of the largest public dataset for Moroccan dialect sentiment analysis that incorporates not only Moroccan dialect written in Arabic script but also in Latin characters. By assembling a diverse range of textual data, we were able to construct a dataset with a range of 19,991 manually labeled texts in Moroccan dialect and also publicly available lists of stop words in Moroccan dialect as a new contribution to Moroccan Arabic resources. In our exploration of sentiment analysis, we undertook a comprehensive study encompassing various machine-learning models to assess their compatibility with our dataset. While our investigation revealed that the highest accuracy of 98.42% was attained through the utilization of the DarijaBert-mix transfer-learning model, we also delved into deep learning models. Notably, our experimentation yielded a commendable accuracy rate of 92% when employing a CNN model. Furthermore, in an effort to affirm the reliability of our dataset, we tested the CNN model using smaller publicly available datasets of Moroccan dialect, with results that proved to be promising and supportive of our findings.

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Acknowledgements

We are grateful to the annotators reported in this article for their help in creating the dataset and for their genuine support.

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Process Engineering, Computing and Mathematics Laboratory, University Sultan Moulay Slimane, Beni-Mellal, Morocco

Mouad Jbel, Mourad Jabrane, Imad Hafidi & Abdulmutallib Metrane

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M.J and J.M created the code to collecte data/comments from youtube videos. M.J and H.I created the procedure to annotate the data and the right strategy to collecte the propper comments. M.A invited two data annotators and participated in the task of annotations too. M.J and M.A and J.M created the code for machine learning models. M.J and H.I wrote the main manuscript and all the figures and tables. All authors reviewed the manuscript. M.J is responsible for submitting the paper.

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Jbel, M., Jabrane, M., Hafidi, I. et al. Sentiment analysis dataset in Moroccan dialect: bridging the gap between Arabic and Latin scripted dialect. Lang Resources & Evaluation (2024). https://doi.org/10.1007/s10579-024-09764-6

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Published : 11 September 2024

DOI : https://doi.org/10.1007/s10579-024-09764-6

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