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.'

翻译：大多数情感计算研究将情感视为文本的静态属性，聚焦于作者的表达情绪而忽略读者的感知视角。这种范式忽视了不同人格特质如何导致同一事件产生差异化情感评估的机制。尽管角色扮演大型语言模型试图模拟此类细微反应，但常陷入"人格幻觉"——依赖表层刻板印象而非真实的认知逻辑。其关键瓶颈在于缺乏将人格特质与情感波动关联的人体真实数据。为解决这一缺口，我们提出Persona-E$^2$（人格-事件至情感映射）大规模数据集，该数据集基于已标注的MBTI与五大人格维度，覆盖新闻、社交媒体及生活叙事场景，捕捉读者主导的情感差异。大量实验表明，当前最先进的大型语言模型难以精准捕捉评估偏移，尤其是在社交媒体领域。关键发现是：人格信息能显著提升理解效果，其中五大人格模型可有效缓解"人格幻觉"。