Context-Aware Sentiment Forecasting via LLM-based Multi-Perspective Role-Playing Agents
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911336509734912 |
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| author | Man, Fanhang Wang, Huandong Fang, Jianjie Deng, Zhaoyi Zhao, Baining Chen, Xinlei Li, Yong |
| author_facet | Man, Fanhang Wang, Huandong Fang, Jianjie Deng, Zhaoyi Zhao, Baining Chen, Xinlei Li, Yong |
| contents | User sentiment on social media reveals the underlying social trends, crises, and needs. Researchers have analyzed users' past messages to trace the evolution of sentiments and reconstruct sentiment dynamics. However, predicting the imminent sentiment of an ongoing event is rarely studied. In this paper, we address the problem of \textbf{sentiment forecasting} on social media to predict the user's future sentiment in response to the development of the event. We extract sentiment-related features to enhance the modeling skill and propose a multi-perspective role-playing framework to simulate the process of human response. Our preliminary results show significant improvement in sentiment forecasting on both microscopic and macroscopic levels. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_24331 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Context-Aware Sentiment Forecasting via LLM-based Multi-Perspective Role-Playing Agents Man, Fanhang Wang, Huandong Fang, Jianjie Deng, Zhaoyi Zhao, Baining Chen, Xinlei Li, Yong Computation and Language User sentiment on social media reveals the underlying social trends, crises, and needs. Researchers have analyzed users' past messages to trace the evolution of sentiments and reconstruct sentiment dynamics. However, predicting the imminent sentiment of an ongoing event is rarely studied. In this paper, we address the problem of \textbf{sentiment forecasting} on social media to predict the user's future sentiment in response to the development of the event. We extract sentiment-related features to enhance the modeling skill and propose a multi-perspective role-playing framework to simulate the process of human response. Our preliminary results show significant improvement in sentiment forecasting on both microscopic and macroscopic levels. |
| title | Context-Aware Sentiment Forecasting via LLM-based Multi-Perspective Role-Playing Agents |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2505.24331 |