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绪视为文本的静态属性，侧重于作者的写作情感而忽视读者视角。这种处理方法忽略了个体人格差异如何导致对同一事件产生多样化的情绪评价。尽管角色扮演大语言模型试图模拟此类细微反应，但它们常陷入"人格幻觉"——依赖表面化的刻板印象而非真实的认知逻辑。关键瓶颈在于缺少将人格特质与情绪转变关联的真实人类数据。为弥补这一不足，我们提出了Persona-E$^2$（Persona-Event2Emotion），一个基于标注的MBTI和大五人格特质构建的大规模数据集，用于捕捉读者在新闻、社交媒体和生活叙事文本上的情绪差异。大量实验表明，当前最先进的大语言模型难以精准捕捉评估转变，尤其在社交媒体领域。关键发现是，人格信息显著提升了理解能力，其中大五人格特质可缓解"人格幻觉"。