Context-Aware Sentiment Forecasting via LLM-based Multi-Perspective Role-Playing Agents
This addresses sentiment forecasting for social media analysis, but it appears incremental as it builds on existing sentiment analysis methods.
The paper tackles the problem of predicting future user sentiment on social media in response to event developments, using a multi-perspective role-playing framework, and reports significant improvement in sentiment forecasting at both microscopic and macroscopic levels.
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.