A comprehensive survey on sentiment analysis: Approaches, challenges and trends

Purpose: This paper provides a comprehensive review of current methods, progress, and open challenges in Sentiment Analysis (SA)—a computational discipline for extracting and interpreting opinions, sentiments, and emotions from text. It aims to identify the limitations of existing approaches and explore pathways toward more generalizable, explainable, and ethically robust sentiment analysis models. Design/Methodology/Approach: The study synthesizes insights from a tertiary review (Lighart et al., 2021), which aggregates findings from 14 systematic literature reviews and mapping studies, and a domain-specific review (Sweta, 2024) focusing on SA in educational contexts. A thematic analysis integrates methodological evolution, major challenges, and emerging trends to produce a consolidated overview of the field’s trajectory. Findings: Results indicate a clear shift from lexicon-based and traditional machine learning approaches to deep learning and transformer-based architectures (e.g., LSTM, CNN, BERT, GPT). Despite significant progress, persistent issues remain—such as domain and language dependency, contextual subtlety (sarcasm, irony), data imbalance, lack of explainability, and ethical concerns regarding privacy. New trends, including cross-domain transfer learning, explainable AI (XAI), multimodal sentiment analysis, and real-time processing, hold promise for overcoming these barriers. Research Limitations/Implications: The review highlights the need for standardized datasets, cross-lingual benchmarks, and interdisciplinary collaboration between NLP researchers and domain experts to enhance model robustness and ethical compliance. Originality/Value: By integrating high-level evidence from tertiary research with practical insights from domain-specific studies, this paper outlines the current landscape and future directions for building generalizable, transparent, and ethically grounded sentiment analysis systems.

The rapid growth of Internet-based applications, such as social media platforms and blogs, has resulted in comments and reviews concerning day-to-day activities. Sentiment analysis is the process of gathering and analyzing people’s opinions, thoughts, and impressions regarding various topics, products, subjects, and services. People’s opinions can be beneficial to corporations, governments, and individuals for collecting information and making decisions based on opinion. However, the sentiment analysis and evaluation procedure face numerous challenges. These challenges create impediments to accurately interpreting sentiments and determining the appropriate sentiment polarity. Sentiment analysis identifies and extracts subjective information from the text using natural language processing and text mining. This article discusses a complete overview of the method for completing this task as well as the applications of sentiment analysis. Then, it evaluates, compares, and investigates the approaches used to gain a comprehensive understanding of their advantages and disadvantages. Finally, the challenges of sentiment analysis are examined in order to define future directions.

Sentiment analysis, a critical task in natural language processing, aims to automatically identify and classify the sentiment expressed in textual data. Aspect-level sentiment analysis focuses on determining sentiment at a more granular level, targeting specific aspects or features within a piece of text. In this paper, we explore various techniques for sentiment analysis, including traditional machine learning approaches and state-of-the-art deep learning models. Additionally, deep learning techniques has been utilized to identifying and extracting specific aspects from text, addressing aspect-level ambiguity, and capturing nuanced sentiments for each aspect. These datasets are valuable for conducting aspect-level sentiment analysis. In this article, we explore a language model based on pre-trained deep neural networks. This model can analyze sequences of text to classify sentiments as positive, negative, or neutral without explicit human labeling. To evaluate these models, data from Twitter's US airlines sentiment database was utilized. Experiments on this dataset reveal that the BERT, RoBERTA and DistilBERT model outperforms than the ML based model in accuracy and is more efficient in terms of training time. Notably, our findings showcase significant advancements over previous state-of-the-art methods that rely on supervised feature learning, bridging existing gaps in sentiment analysis methodologies. Our findings shed light on the advancements and challenges in sentiment analysis, offering insights for future research directions and practical applications in areas such as customer feedback analysis, social media monitoring, and opinion mining.

Audio sentiment analysis is a rapidly evolving field that seeks to uncover emotional insights from audio signals, with broad applications in technology, healthcare, and user experience design. This paper presents a detailed review of recent advancements, highlighting key techniques, ongoing challenges, and real-world applications that illustrate the potential of this technology. The Literature Review provides an integrated analysis of significant findings across various studies, identifying core trends and unique contributions. Finally, the Conclusion explores promising future research directions, laying the groundwork for continued innovation in extracting sentiment from sound. Keywords Audio Sentiment Analysis, Sentiment Classification, Deep Learning Techniques, Feature Extraction, Transfer Learning Ensemble Methods, Multi-Task Learning, Challenges in Sentiment Analysis, Applications of Sentiment Analysis, Literature Review of Audio Sentiment Analysis

Purpose: This paper reviews the current state, progress, and challenges of Sentiment Analysis (SA), a computational approach to identifying and interpreting human opinions and emotions from text. It aims to evaluate the evolution of methods, highlight persistent limitations, and explore emerging directions toward developing generalizable, explainable, and ethically responsible sentiment analysis systems. Design/Methodology/Approach: The review integrates evidence from two key sources: (1) a tertiary study (Lighart et al., 2021) summarizing outcomes from 14 systematic literature reviews and mapping studies, and (2) a domain-specific review (Sweta, 2024) on sentiment analysis in education. Through thematic analysis, the study identifies methodological trends, core challenges, and innovations shaping the discipline, linking large-scale findings with domain-level applications. Findings: The analysis reveals a significant methodological transition—from lexicon-based and traditional machine learning models (SVM, Naive Bayes) to deep learning and transformer-based architectures (CNN, LSTM, BERT, GPT). Despite progress, key issues persist: domain and language dependency, lack of contextual sensitivity (sarcasm, irony), data imbalance, model opacity, and privacy concerns. New directions, such as cross-domain and multilingual modeling, explainable AI (XAI), multimodal analysis, and real-time SA, demonstrate promising solutions. Research Limitations/Implications: The review highlights the need for cross-lingual datasets, explainable architectures, and interdisciplinary collaborations to enhance generalizability, fairness, and ethical compliance. Originality/Value: By combining high-level synthesis with domain-specific insights, this work provides a comprehensive and forward-looking framework for building robust, transparent, and socially responsible sentiment analysis models.

Sentiment analysis (SA) is a subject of ongoing text mining research. The subjectivity, emotions, and viewpoints of a text are handled algorithmically by sentiment analysis. This survey research examines the most recent development in this field in great detail. This paper examines and provides a quick overview of various recently suggested algorithm enhancements. Based on their contributions to the various sentiment analysis approaches, these articles are categorized into various categories. Sentiment analysis may be utilised to extract information related to thoughts and feelings from human-generated textual data. Users have posted a significant quantity of unprocessed material to social networking sites in the shape of speech, videos, photographs, and audio. Global catastrophes including heatwaves, earthquakes, cyclones, floods and bushfires are having an extraordinary impact on social media users' life. They frequently write how pessimistic they find the catastrophic scenarios at their target area to be. Politicians and strategic planners must prioritise comprehending location-specific attitudes towards crisis situations. Data may be turned into valuable knowledge by using sentiment analysis. The primary goal of the questionnaire is to provide a concise, almost complete picture of Sentiment Analysis methods and associated topics. The comprehensive categories of several recently published papers and the explanation of the present state of research in sentiment evaluation as well as related fields are the key contributions made by this study.

Even though sociolinguistic and psycholinguistic studies demonstrate that the qualities of the speaker and the intended audience have an impact on the message that is transmitted, the majority of NLP models used today assume language as universal. The difficulty of sentiment analysis, often known as opinion mining, in natural language processing (NLP) is one that is currently being faced. It’s also challenging for people to share their ideas on websites like Facebook, Twitter, and Yelp since innovation has grown dramatically. Social media’s widespread use has resulted in a huge volume of data generation, including evaluations, comments, and opinions. Researchers claim that the phrase, document, aspect, and user levels are the foundations for sentiment analysis. This data requires more time to analyse and is challenging to process. In order to categorize or determine positive, negative, and neutral opinions, an intelligent system must be developed.

The state of Meghalaya, which is one of India’s seven north-eastern states and shares an international border with Bangladesh, boasts 1.6 million native speakers of the Khasi language (according to the 2001 census). The main objective of this paper is to provide an overview of the most recent techniques and models for sentiment analysis, with special attention to low-resource languages like the Khasi language. The survey summarises the popular data sets and models applied to them, the accuracy obtained from them, and also a comparison of these models. This paper also highlighted the scope and challenges of sentiment analysis and emotion detection on Khasi text data. Finally, a future directions are provided for research work with sentiment analysis with Khasi text data.

This study addresses challenges in sentiment analysis for low-resource educational contexts by proposing a framework that integrates Few-Shot Learning (FSL) with Transformer-based ensemble models and boosting techniques. Sentiment analysis of student feedback is crucial for improving teaching quality, yet traditional methods struggle with data scarcity and computational inefficiency. The proposed framework leverages self-attention mechanisms in Transformers and combines models through Gradient Boosting to enhance performance and generalization with minimal labeled data. Evaluated on the UIT-VSFC dataset, comprising Vietnamese student feedback, the framework achieved superior F1-scores in sentiment and topic classification tasks, outperforming individual models. Results demonstrate their potential for extracting actionable insights to enhance educational experiences. Despite its effectiveness, the approach faces limitations such as reliance on pre-trained models and computational complexity. Future work could optimize lightweight models and explore applications in other domains like healthcare and finance.

Modality translation-based multimodal sentiment analysis under uncertain missing modalities

Everyone in this rapid evolution generation makes use of online resources. There is a huge amount of textual content available online from several sources, including social media apps, e-commerce sites, movie recommendation system, etc. Everyone in the modern society provides a variety of reviews for a specific product or item. An effective and smart technique is required that can analyze and provide the polarity of this textual data. since it is impossible to manually review such a vast volume of user-generated data. For this user-generated data, automatic sentiment classification is possible using a variety of tools and methods. Opinion mining and sentiment classification benefit from the dependable and accuracy of machine learning. Moreover, the sentiment analysis and evaluation process are fraught with difficulties. These complexities make it difficult to interpret sentiments accurately and figure out the right sentiment polarity. This article describes an overview on sentiment analysis(SA) applications, challenges, and issues encountered by researchers, with the goal of providing a systematic review on sentiment analysis and related areas for future research.

Social media sentiment analysis (also known as opinion mining) which aims to extract people's opinions, attitudes and emotions from social networks has become a research hotspot. Conventional sentiment analysis concentrates primarily on the textual content. However, multimedia sentiment analysis has begun to receive attention since visual content such as images and videos is becoming a new medium for self-expression in social networks. In order to provide a reference for the researchers in this active area, we give an overview of this topic and describe the algorithms of sentiment analysis and opinion mining for social multimedia. Having conducted a brief review on textual sentiment analysis for social media, we present a comprehensive survey of visual sentiment analysis on the basis of a thorough investigation of the existing literature. We further give a summary of existing studies on multimodal sentiment analysis which combines multiple media channels. We finally summarize the existing benchmark datasets in this area, and discuss the future research trends and potential directions for multimedia sentiment analysis. This survey covers 100 articles during 2008---2018 and categorizes existing studies according to the approaches they adopt.

Nowadays, with the rapid development of social networks, community-oriented Web sentiment analysis technology has gradually become a hot topic in the field of data mining. Being concise and flexible, Chinese microblog poses new challenges for sentiment analysis. This paper proposes an approach to classify Chinese microblog sentiments into positive and negative by the plain Naive Bayes (NB) and Support Vector Machine (SVM). Based on data preprocessing, sentiment lexicon construction, combining element of users’ comments, this research posit this Comments-attached Microblog Sentiment Classification, which is a novel method of attaching microblog users’ comments to the target microblog in order to improve the accuracy of sentiment classification. The experiment proves the vitality of this method and the advancement of the indecency from the way of language expressions.KeywordsChinese Microblogsentiment classificationmachine learninguser’s comments

Sentiment analysis has become a powerful tool in processing and analysing expressed opinions on a large scale. While the application of sentiment analysis on English-language content has been widely examined, the applications on the Russian language remains not as well-studied. In this survey, we comprehensively reviewed the applications of sentiment analysis of Russian-language content and identified current challenges and future research directions. In contrast with previous surveys, we targeted the applications of sentiment analysis rather than existing sentiment analysis approaches and their classification quality. We synthesised and systematically characterised existing applied sentiment analysis studies by their source of analysed data, purpose, employed sentiment analysis approach, and primary outcomes and limitations. We presented a research agenda to improve the quality of the applied sentiment analysis studies and to expand the existing research base to new directions. Additionally, to help scholars selecting an appropriate training dataset, we performed an additional literature review and identified publicly available sentiment datasets of Russian-language texts.

The rapid growth of the video game industry and its reliance on digital distribution have created new opportunities for data-driven sales forecasting. Social media platforms serve as influential environments where consumer sentiment, trends, and discussions impact purchasing behaviors. This study examines the potential of using sentiment analysis of social media data to predict video game sales. While traditional sales forecasting models mainly depend on historical sales data and statistical techniques, sentiment analysis offers real-time insights into consumer interest and market demand. This paper reviews existing research on video game sales prediction, the application of sentiment analysis in the gaming industry, and sentiment-based forecasting models in other domains. The findings highlight a significant research gap in applying sentiment analysis to video game sales forecasting, despite its demonstrated efficacy in related fields. The study emphasizes the advantages and challenges of integrating sentiment analysis with traditional forecasting methods and proposes future research directions to enhance predictive accuracy.