Persona-E$^2$: 一种基于人类的用于个性塑造文本事件情感反应的数据集
Persona-E$^2$: A Human-Grounded Dataset for Personality-Shaped Emotional Responses to Textual Events
AI总结：
本文提出Persona-E$^2$数据集，通过标注MBTI和Big Five特质，捕捉读者对新闻、社交媒体和生活叙事的情感变化，发现性格信息显著提升理解，缓解'性格幻觉'。
摘要：
大多数情感计算研究将情绪视为文本的静态属性，专注于作者的情感而忽视读者视角。此方法忽略了个体性格导致对同一事件产生多样化情感评估。尽管角色扮演大型语言模型（LLMs）试图模拟这种细腻反应，但往往受'性格幻觉'限制，依赖表面刻板印象而非真实认知逻辑。关键瓶颈是缺乏真实人类数据将性格特征与情绪变化联系起来。为弥合这一差距，我们引入Persona-E$^2$（Persona-Event2Emotion），一个大规模数据集，基于标注的MBTI和Big Five特质，捕捉新闻、社交媒体和生活叙事中的读者情感变化。广泛实验表明，最先进的LLMs在捕捉精确评估变化方面存在困难，特别是在社交媒体领域。关键发现是，性格信息显著提升理解，Big Five特质缓解'性格幻觉'。
英文摘要：
Most affective computing research treats emotion as a static property of text, focusing on the writer's sentiment while overlooking the reader's perspective. This approach ignores how individual personalities lead to diverse emotional appraisals of the same event. Although role-playing Large Language Models (LLMs) attempt to simulate such nuanced reactions, they often suffer from "personality illusion'' -- relying on surface-level stereotypes rather than authentic cognitive logic. A critical bottleneck is the absence of ground-truth human data to link personality traits to emotional shifts. To bridge the gap, we introduce Persona-E$^2$ (Persona-Event2Emotion), a large-scale dataset grounded in annotated MBTI and Big Five traits to capture reader-based emotional variations across news, social media, and life narratives. Extensive experiments reveal that state-of-the-art LLMs struggle to capture precise appraisal shifts, particularly in social media domains. Crucially, we find that personality information significantly improves comprehension, with the Big Five traits alleviating "personality illusion.'
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
