Persona-E$^2$: A Human-Grounded Dataset for Personality-Shaped Emotional Responses to Textual Events
Yuqin Yang, Haowu Zhou, Haoran Tu, Zhiwen Hui, Shiqi Yan + 5 more
TLDR
Persona-E$^2$ is a new human-grounded dataset linking personality traits to diverse emotional responses to text, revealing LLM struggles and improving comprehension.
Key contributions
- Introduces Persona-E$^2$, a large-scale dataset linking human personality (MBTI/Big Five) to emotional responses.
- Captures reader-based emotional variations across news, social media, and life narratives.
- Reveals state-of-the-art LLMs struggle to capture precise emotional shifts, especially in social media.
- Demonstrates personality information, particularly Big Five traits, significantly improves LLM comprehension.
Why it matters
This paper addresses a critical gap in affective computing by providing the first human-grounded dataset linking personality to emotional responses. It highlights current LLM limitations in understanding nuanced emotions, especially on social media. This work is crucial for developing more authentic and personalized emotionally intelligent AI.
Original 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 domains. Crucially, we find that personality information significantly improves comprehension, with the Big Five traits alleviating "personality illusion.'
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