Download pdf
Literature Review of Text and Multimodal Sentiment Analysis
- 1 School of Computing, Beijing Institution of Technology, Beijing, China, 100081
* Author to whom correspondence should be addressed.
Abstract
Sentiment analysis, also known as opinion mining, is a crucial branch of Natural language processing, which focuses on recognizing, extracting, and quantifying sentiment tendencies, emotional intensity and specific emotion types in textual data. With the rapid development of the internet and communication, analyzing sentiment contained in textual data becomes important and crucial for understanding public opinion, consumer behavior, and emotional trends. This paper provides a comprehensive review of sentiment analysis in the range of its application, evolution, task types, methodology and future development by analyzing the literature of this field. Sentiment analysis has developed from traditional lexicon-based methods to modern deep learning methods like CNN, RNN and transformer model, which have significantly improved accuracy and robustness. This paper also discussed challenges in sentiment analysis like sarcasm detection and cross-lingual analysis, and proposed potential solutions. The findings aim to provide comprehensive insight for researchers and contribute to innovations in sentiment analysis.
Keywords
sentiment analysis, artificial intelligence, multimodal, literature review
[1]. S. Baccianella, A. Esuli, F. Sebastiani, SentiWordNet, Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining, in: Proc. Int. Conf. Lang. Resour. Eval. {LREC} 2010, 17-23 2010, European Language Resources Association, Valletta, Malta, 2010, pp. 1-5, http: //www.lrec-conf.org/proceedings/lrec2010/pdf/769_Paper.pdf.
[2]. Zezawar, T.K., Aung, N.M., Sentiment analysis of students' comment using lexicon based approach. IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS), 2017.
[3]. J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding, 2018, pp. 1-16, http://arxiv.org/abs/1810.04805.
[4]. HU M, LIU B. Mining and summarizing customer reviews[C/OL]//Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. 2004. http://dx.doi.org/10.1145/1014052.1014073. DOI:10.1145/1014052.1014073.
[5]. LI W, QI F, TANG M, et al. Bidirectional LSTM with self-attention mechanism and multi-channel features for sentiment classification[J/OL]. Neurocomputing, 2020: 63-77. http://dx.doi.org/10.1016/j.neucom.2020.01.006. DOI:10.1016/j.neucom.2020.01.006.
[6]. C.E. Izard. Innate and universal facial expressions: Evidence from developmental and cross-cultural research. Psychological Bulletin, 115(2): 288-299, 1994.
[7]. R. W. Picard, E. Vyzas, J. Healey. “Toward machine emotional intelligence: Analysis of affective physiological state,” IEEE Transactionas on Pattern Analysis and Machine Intelligence, vol. 23, pp. 1175-1191, 2001.
[8]. Poria S, Cambria E, Bajpai R, Hussain A. A review of affective computing: From unimodal analysis to multimodal fusion.Information Fusion, 2017, 37: 98-125.
[9]. Micol Spitale, Fabio Catania, and Francesca Panzeri. 2024. Understanding Non-Verbal Irony Markers: Machine Learning Insights Versus Human Judgment. In International Conference on Multimodal Interaction (ICMI '24), November 04--08, 2024, San Jose, Costa Rica. ACM, New York, NY, USA 9 Pages. https://doi.org/10.1145/3678957.3685723
[10]. Barnes J, Klinger R. Embedding projection for targeted cross-lingual sentiment: Model comparisons and a real-world study[J]. Journal of Artificial Intelligence Research, 2019, 66: 691-742-691-742.
Cite this article
Li,Y. (2025). Literature Review of Text and Multimodal Sentiment Analysis. Applied and Computational Engineering,150,149-154.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 3rd International Conference on Software Engineering and Machine Learning
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).