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home / papers / 2026 / Persona-E\texttwosuperior: A Human-Grounded Dataset for Personality-Shaped Emotional Responses to Textual Events

Persona-E\texttwosuperior: A Human-Grounded Dataset for Personality-Shaped Emotional Responses to Textual Events

Paper · 2026 · Yuqin Yang, Haowu Zhou, Haoran Tu et al.

Summary

Most emotion resources in NLP label a text with one emotion, usually the writer's. They ignore that different readers can react quite differently to the same event depending on their personality. LLM role-play can be prompted to simulate such readers, but it tends toward "personality illusion", meaning stereotyped behaviour rather than real trait-driven appraisal. Until now there has been no human ground truth to test it against.

The authors built Persona-E² (Persona-Event2Emotion) to fill this gap. They gathered events from news, Reddit and Social Chemistry 101, and life-experience narratives. These passed through safety filtering, LLM-based scoring and expert checks. Then 36 annotators, each profiled with MBTI and Big Five (BFI) scores, reported how each event made them feel.

Experiments show that emotional appraisal depends on the domain, and that annotator disagreement carries a systematic personality signal. Current LLMs fail to predict fine-grained shifts in appraisal, especially for social media. Adding personality information helps them, and BFI profiles reduce personality illusion more than MBTI profiles do. The resource supports evaluation of personalised and empathetic AI that is grounded in real human responses.

Key points

  • Persona-E² has 3,111 events and 111,996 annotations. Every event was labelled by all 36 annotators, giving 36 labels per event. The labels use Ekman's six emotions plus neutral.
  • The candidate pool was cut from 76,773 to 6,348 events using Qwen3-Max. The model translated non-English texts and scored events on a weighted mix of personality variability (0.35), arousal (0.30), source relevance (0.20) and implicitness (0.15).
  • Annotators answered a reader-centric question in the style of ISEAR without any role-play. Each annotation unit stores the event, the annotator ID, the measured MBTI/BFI traits and the emotion label.
  • The study is organised around three research questions: affective divergence across writer, general reader and persona reader; LLM simulation of persona readers; and the psychological soundness of model rationales.
  • The data is on Hugging Face (CRIS-Yang/Persona-E2-Dataset) and Kaggle for non-commercial research only. The annotator pool has a known limitation: all were university-educated and aged 18–25.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓South China University of TechnologyGuangzhou · China27 works · 1,554 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksPsychology · 1,649 worksExperimental and Cognitive Psychology · 930 worksHuman-Computer Interaction · 160 works

🧭 Topics 1Research subjects and languages the work is about.Sentiment & emotion · 1,035 works

Cite

Cite as Yuqin Yang, Haowu Zhou, Haoran Tu, Zhiwen Hui, Shiqi Yan, HaoYang Li, Dong She, Xianrong Yao, Yang Gao and Zhanpeng Jin. 2026. Persona-E\texttwosuperior: A Human-Grounded Dataset for Personality-Shaped Emotional Responses to Textual Events. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.1350

@inproceedings{yang2026persona,
  title = {{Persona-Etexttwosuperior: A Human-Grounded Dataset for Personality-Shaped Emotional Responses to Textual Events}},
  author = {Yuqin Yang, Haowu Zhou, Haoran Tu, Zhiwen Hui, Shiqi Yan, HaoYang Li, Dong She, Xianrong Yao, Yang Gao and Zhanpeng Jin},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1350},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1350}
}

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