@inproceedings{yang-etal-2026-persona-e2,
title = "Persona-E{\texttwosuperior}: A Human-Grounded Dataset for Personality-Shaped Emotional Responses to Textual Events",
author = "Yang, Yuqin and
Zhou, Haowu and
Tu, Haoran and
Hui, Zhiwen and
Yan, Shiqi and
Li, HaoYang and
She, Dong and
Yao, Xianrong and
Gao, Yang and
Jin, Zhanpeng",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.1350/",
pages = "29290--29315",
ISBN = "979-8-89176-390-6",
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{\texttwosuperior} (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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<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-E² (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.’</abstract>
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%0 Conference Proceedings
%T Persona-E²: A Human-Grounded Dataset for Personality-Shaped Emotional Responses to Textual Events
%A Yang, Yuqin
%A Zhou, Haowu
%A Tu, Haoran
%A Hui, Zhiwen
%A Yan, Shiqi
%A Li, HaoYang
%A She, Dong
%A Yao, Xianrong
%A Gao, Yang
%A Jin, Zhanpeng
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F yang-etal-2026-persona-e2
%X 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-E² (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.’
%U https://aclanthology.org/2026.acl-long.1350/
%P 29290-29315
Markdown (Informal)
[Persona-E²: A Human-Grounded Dataset for Personality-Shaped Emotional Responses to Textual Events](https://aclanthology.org/2026.acl-long.1350/) (Yang et al., ACL 2026)
ACL
- Yuqin Yang, Haowu Zhou, Haoran Tu, Zhiwen Hui, Shiqi Yan, HaoYang Li, Dong She, Xianrong Yao, Yang Gao, and Zhanpeng Jin. 2026. Persona-E²: A Human-Grounded Dataset for Personality-Shaped Emotional Responses to Textual Events. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 29290–29315, San Diego, California, United States. Association for Computational Linguistics.