Issue |
ITM Web Conf.
Volume 73, 2025
International Workshop on Advanced Applications of Deep Learning in Image Processing (IWADI 2024)
|
|
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Article Number | 03006 | |
Number of page(s) | 7 | |
Section | Blockchain, AI, and Technology Integration | |
DOI | https://doi.org/10.1051/itmconf/20257303006 | |
Published online | 17 February 2025 |
Current Status and Future Prospects of Sentiment Analysis in Social Media Texts
Faculty of Innovation Engineering, Macau University of Science and Technology, 999078, Macau, China
* Corresponding author: 1230026907@student.must.edu.mo
With the flourishing of social media, its diversification, high interactivity, and richness of content are becoming increasingly significant, which has a profound impact on people's communication modes and information acquisition channels. Sentiment analysis of massive and diverse social media text data has become an important tool for grasping users' emotional tendencies and attitudes accurately. This paper aims to comprehensively analyze the current situation of social media text sentiment analysis. It focuses on three classic analysis paths, analyzes the strengths and weakness of each approach respectively, and on this basis sorts out the research methods and achievements of each researchers. In addition, this paper also analyzes the limitations of the current social media text sentiment analysis, discusses in detail the challenges and opportunities of the practical application of these analysis methods, and looks forward to the future development trend. This paper aims to provide a certain theoretical foundation and practical guidance for future related research and promote the further development and optimization of sentiment analysis techniques in the field of social media, to play a more active role in understanding public sentiment, guiding marketing strategies, and monitoring social opinions.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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