@inproceedings{man-etal-2025-context,
    title = "Context-Aware Sentiment Forecasting via {LLM}-based Multi-Perspective Role-Playing Agents",
    author = "Man, Fanhang  and
      Wang, Huandong  and
      Fang, Jianjie  and
      Deng, Zhaoyi  and
      Zhao, Baining  and
      Chen, Xinlei  and
      Li, Yong",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.136/",
    pages = "2687--2703",
    ISBN = "979-8-89176-251-0",
    abstract = "User sentiment on social media reveals underlying social trends, crises, and needs. Researchers have analyzed users' past messages to track the evolution of sentiments and reconstruct sentiment dynamics. However, predicting the imminent sentiment response of users to ongoing events remains understudied. In this paper, we address the problem of sentiment forecasting on social media to predict users' future sentiment based on event developments. We extract sentiment-related features to enhance modeling and propose a multi-perspective role-playing framework to simulate human response processes. Our preliminary results show significant improvements in sentiment forecasting at both microscopic and macroscopic levels."
}