Multimodal Emotion Recognition and Fluctuation: A Study on Sentiment Analysis of Online Public Opinion
DOI:
https://doi.org/10.54097/fcis.v3i1.6021Keywords:
Emotion, Online Public Opinion, Sentiment AnalysisAbstract
Emotion is an important way for individuals to express their views on the Internet and an important variable that shapes public opinion. Considering the multimodality of data, such as text, picture and video, and the subtlety of emotional expression, a multimodal sentiment analysis model that addresses content involving difference senses, such as sight, hearing and touch at the same time is very necessary. This study outlines the basic steps, classification strategies and research methods of sentimental analysis and acknowledges the differences between sentimental analyses on text, picture and video. As multimodal sentiment recognition is still in its initial stage, there’s still room for improvement in cross-disciplinary research on multimodal data of text, picture, audio, video in terms of weighted scoring, complex emotion and intensity recognition. It’s concluded that future studies should focus on the intensity of different emotions, multimodal data fusion and how weighted scoring influences an emotion recognition model and explore application possibilities.
Downloads
References
CHEN Xiaoting, Li Shi, ”Survey on Emotion Recognition in Conversation,” in Computer Engineering and Applications, vol. 3, J. 2023, pp.33-48.
Read J, ”Using Emoticons to Reduce Dependency in Machine Learning Techniques for Sentiment Classification（Presented Conference Paper style),” presented at Proc of the 43rd ACL Student Research Workshop, Stroudsburg.
Li Ran, Lin Zheng, Lin Hailun, Wang Weiping, Meng Dan, “Text Emotion Analysis: A Survey” in Journal of Computer Research and Development, vol. 55, J. 2018, pp. 30-52.
Bolen J, Mao H, Zeng X, “Twiter mood predicts the stock market,” in Journal of Computational Science, vol. 2, J.2011, pp.1 -8.
Zhang M, Zheng R, Chen J, et al, “ Emotional Component Analysis and Forecast Public Opinion on Micro-blog Posts Based on Maximum Entropy Model, ” in Cluster Computing, vol. 3, J. 2019, pp.6295-6304.
LiL, WuY, Zhang, etal, “Time+user Dual Attention Based Sentiment Prediction for Multiple Social Network Texts with Time Series, ”IEEE-Access, vol.7, J. 2019, pp. 17644-17653.
Majumder N, Hazarika D, Gelbukh A, et al, “Multimodal Sentiment Analysis Using Hierarchical Fusion with Context Modeling, ”in Knowledge-based systems, vol.161, J.2018, pp.124-133.
Shi Wei, Xue Guangcong, He Shaoyi, “Literature Review of Network Public Opinion Research from the Perspective of Sentiment ”in Documentation, Information & Knowledge, vol.39, J. 2022, pp.105-118.
Plutchik R, “The Nature of emotions, ” in Philosophical Studies, vol.4, J.2001, pp. 393-409.
Ekman P, “An Argument for Basic Emotions,”in Cognition and Emotion, vol.6, J.1992, pp.169-200.
Parrott W G, Emotions in Social Psychology: Esential Readings. Oxford, UK: Psychology Press, 2001.
Mishne G,“Experiments with Mood Clasification in Blog-Posts, ”presented at Proc of the 28th ACM SIGIR Workshop on Stylistic Analysis of Text for Information Access, New York.
Shen Lining, Yang Jiayi, Pei Jiaxuan, Cao Guang, Chen Gongzheng, “A Fine-grained Emotion Recognition Method Based on OCC-Model and Emotion Cause Event Extraction (Unpublished work style)” unpublished.
Pang B, Lee L, “Thumbs up? Sentiment classification using machine learning techniques”in Empirical Methods in Natural Language Processing , vol.9, J.2022, pp. 79-86.
Poria S, Cambria E, Howard N, et al, “Fusing Audio, Visual and Textual Clues for Sentiment Analysis from Multimodal Content, ”in Neurocomputing, vol.174, 2016, pp. 50-59.
Zhao Yicheng, Wang Suge,“Image-text aspect emotion recognition based on joint aspect attention interaction (Unpublished work style) , ” unpublished.
CaoD,JiR,“VisualSentimentTopicModelBasedMicroblogImageSentimentAnalysis[J]. Multimedia Tools and Applications ”in Multimedia Tools and Applications, vol. 75,2016, pp.8955-8968.
HUANG Jian, WANG Ying, “Image-text fusion sentiment analysis method based on image semantic translation (Unpublished work style) , ” unpublished.
LIU Qiwei, LI Jun, GU Beibei, ZHAO Zefang, “TSAIE: Text Sentiment Analysis Model Based on Image Enhancement”in Frontiers of Data & Computing, vol. 4, J.2022, pp. 131-140.
