[2604.09162] Persona-E$^2$: A Human-Grounded Dataset for Personality-Shaped Emotional Responses to Textual Events

Computer Science > Computation and Language arXiv:2604.09162 (cs) [Submitted on 10 Apr 2026] Title:Persona-E$^2$: A Human-Grounded Dataset for Personality-Shaped Emotional Responses to Textual Events Authors:Yuqin Yang, Haowu Zhou, Haoran Tu, Zhiwen Hui, Shiqi Yan, HaoYang Li, Dong She, Xianrong Yao, Yang Gao, Zhanpeng Jin View a PDF of the paper titled Persona-E$^2$: A Human-Grounded Dataset for Personality-Shaped Emotional Responses to Textual Events, by Yuqin Yang and 9 other authors View PDF HTML (experimental) Abstract: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 domai...