用多角色模拟预测用户对事件的情绪变化。
Context-Aware Sentiment Forecasting via LLM-based Multi-Perspective Role-Playing Agents
- 设计多视角角色扮演框架模拟人类情绪反应过程。
- 在微观与宏观层面均显著提升情绪预测准确率。
- 适合研究社交媒体情绪演化与舆情分析的学者。
社交媒体上的用户情绪反映了潜在的社会趋势、危机与需求。研究人员通过分析用户的历史消息来追踪情绪演变并重构情绪动态，但对正在进行事件中用户情绪的即时预测仍很少被研究。本文针对社交媒体中的情绪预测问题，旨在预测用户对事件发展所产生未来情绪。通过提取情绪相关特征增强建模能力，并提出一种基于多视角角色扮演的框架，模拟人类响应过程。初步结果表明，在微观和宏观层面均实现显著性能提升。
原文摘要 · Abstract (English)
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.
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