标题： 使用大语言模型推断中国微博用户的非二元新冠情感 显示英文标题

标题： Using LLMs to Infer Non-Binary COVID-19 Sentiments of Chinese Micro-bloggers

摘要： 研究危机期间的公众情绪对于理解观点和情绪的变化至关重要，这导致了社会的两极分化。 我们研究了微博，这是中国最受欢迎的微博网站，在新冠疫情爆发期间发布的帖子。 研究时期包括疫情前阶段、爆发阶段和疫情防控的早期阶段。 我们使用Llama 3 8B，一个大型语言模型，通过将用户情绪分类为积极、消极、讽刺和中性类别来分析平台上的用户情绪。 分析微博上的情绪变化提供了对社会事件和政府行动如何影响公众意见的见解。 这项研究有助于理解健康危机期间社会情绪的动态，弥补了对中国平台情感分析的空白。 通过研究这些动态，我们旨在提供关于数字通信在塑造社会应对前所未有的全球挑战中的作用的有价值的观点。 显示英文摘要

摘要： Studying public sentiment during crises is crucial for understanding how opinions and sentiments shift, resulting in polarized societies. We study Weibo, the most popular microblogging site in China, using posts made during the outbreak of the COVID-19 crisis. The study period includes the pre-COVID-19 stage, the outbreak stage, and the early stage of epidemic prevention. We use Llama 3 8B, a Large Language Model, to analyze users' sentiments on the platform by classifying them into positive, negative, sarcastic, and neutral categories. Analyzing sentiment shifts on Weibo provides insights into how social events and government actions influence public opinion. This study contributes to understanding the dynamics of social sentiments during health crises, fulfilling a gap in sentiment analysis for Chinese platforms. By examining these dynamics, we aim to offer valuable perspectives on digital communication's role in shaping society's responses during unprecedented global challenges.