@inproceedings{hanafusa-etal-2026-personalized,
title = "Personalized Emotion Analysis via Prompting Large Language Models with Writer Information",
author = "Hanafusa, Kentaro and
Maekawa, Daisuke and
Kajiwara, Tomoyuki",
editor = "Bak, JinYeong and
van der Goot, Rob and
Jang, Hyeju and
Buaphet, Weerayut and
Ramponi, Alan and
Xu, Wei and
Ritter, Alan",
booktitle = "Proceedings of the 11th Workshop on Natural User-generated Text",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.wnut-1.16/",
pages = "217--226",
ISBN = "979-8-89176-470-5",
abstract = "Personalized emotion analysis aims to account for individual differences in emotional expression by adapting predictions to each writer.Existing personalization methods typically require dedicated feature extraction modules or modifications to model architectures, increasing model complexity. In this paper, we propose a prompt-based personalization framework for emotion analysis that incorporates writer information into the input prompt of a large language model (LLM), without requiring fine-tuning or architectural changes.Specifically, we investigate two complementary types of writer information: personality traits representing stable writer characteristics and post history representing writer-specific patterns of emotional expression. Experimental results on the WRIME corpus in Japanese show that incorporating temporally close post history consistently improves emotion intensity estimation across multiple LLMs. Personality information alone provides limited improvements, but yields additional gains for several models when combined with post history. These findings suggest that prompt-based personalization is a practical alternative to architecture-level personalization for LLM-based emotion analysis."
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<abstract>Personalized emotion analysis aims to account for individual differences in emotional expression by adapting predictions to each writer.Existing personalization methods typically require dedicated feature extraction modules or modifications to model architectures, increasing model complexity. In this paper, we propose a prompt-based personalization framework for emotion analysis that incorporates writer information into the input prompt of a large language model (LLM), without requiring fine-tuning or architectural changes.Specifically, we investigate two complementary types of writer information: personality traits representing stable writer characteristics and post history representing writer-specific patterns of emotional expression. Experimental results on the WRIME corpus in Japanese show that incorporating temporally close post history consistently improves emotion intensity estimation across multiple LLMs. Personality information alone provides limited improvements, but yields additional gains for several models when combined with post history. These findings suggest that prompt-based personalization is a practical alternative to architecture-level personalization for LLM-based emotion analysis.</abstract>
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%0 Conference Proceedings
%T Personalized Emotion Analysis via Prompting Large Language Models with Writer Information
%A Hanafusa, Kentaro
%A Maekawa, Daisuke
%A Kajiwara, Tomoyuki
%Y Bak, JinYeong
%Y van der Goot, Rob
%Y Jang, Hyeju
%Y Buaphet, Weerayut
%Y Ramponi, Alan
%Y Xu, Wei
%Y Ritter, Alan
%S Proceedings of the 11th Workshop on Natural User-generated Text
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-470-5
%F hanafusa-etal-2026-personalized
%X Personalized emotion analysis aims to account for individual differences in emotional expression by adapting predictions to each writer.Existing personalization methods typically require dedicated feature extraction modules or modifications to model architectures, increasing model complexity. In this paper, we propose a prompt-based personalization framework for emotion analysis that incorporates writer information into the input prompt of a large language model (LLM), without requiring fine-tuning or architectural changes.Specifically, we investigate two complementary types of writer information: personality traits representing stable writer characteristics and post history representing writer-specific patterns of emotional expression. Experimental results on the WRIME corpus in Japanese show that incorporating temporally close post history consistently improves emotion intensity estimation across multiple LLMs. Personality information alone provides limited improvements, but yields additional gains for several models when combined with post history. These findings suggest that prompt-based personalization is a practical alternative to architecture-level personalization for LLM-based emotion analysis.
%U https://aclanthology.org/2026.wnut-1.16/
%P 217-226
Markdown (Informal)
[Personalized Emotion Analysis via Prompting Large Language Models with Writer Information](https://aclanthology.org/2026.wnut-1.16/) (Hanafusa et al., WNUT 2026)
ACL