arXiv 2412.073672025-05-22cs.CL

我的话暗示你的观点：基于读者智能体的传播增强用于个性化隐式情感分析

My Words Imply Your Opinion: Reader Agent-based Propagation Enhancement for Personalized Implicit Emotion Analysis

School of Computer and Information Technology, Shanxi University, China（山西大学计算机与信息学院）；Institute of Automation, Chinese Academy of Science, China（中国科学院自动化研究所）；Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, China（教育部计算智能与中文信息处理重点实验室）；Joint Laboratory of Tourism Big Data in Shanxi Province, China（山西省旅游大数据联合实验室）

Jian Liao, Yu Feng, Yujin Zheng, Jun Zhao, Suge Wang, Jianxing Zheng

发表信息：: Proceedings of the 63th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (ACL2025)

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AI总结：

针对个性化隐式情感分析忽视读者影响的问题，提出RAPPIE模型，利用大语言模型模拟读者反馈并构建角色感知多视图图学习，在两个新数据集上显著优于基线。

摘要：

情感表达的微妙性使得隐式情感分析（IEA）对用户特定特征尤为敏感。现有研究通过关注作者来实现情感分析的个性化，但忽视了预期读者对隐式情感反馈的影响。在本文中，我们提出了个性化隐式情感分析（PIEA）任务，并介绍了RAPPIE模型，该模型通过纳入读者反馈来解决主观变异性问题。具体而言，（1）我们基于大语言模型创建读者智能体来模拟读者反馈，克服了真实读者反应中“沉默螺旋效应”和数据不完整的问题。（2）我们开发了一种角色感知的多视图图学习方法，以建模在读者信息稀疏场景下的情感交互传播过程。（3）我们构建了两个新的PIEA数据集，涵盖英文和中文社交媒体，并包含详细的用户元数据，解决了现有数据集仅以文本为中心的局限性。大量实验表明，RAPPIE显著优于最先进的基线方法，证明了在PIEA中纳入读者反馈的价值。

英文摘要：

The subtlety of emotional expressions makes implicit emotion analysis (IEA) particularly sensitive to user-specific characteristics. Current studies personalize emotion analysis by focusing on the author but neglect the impact of the intended reader on implicit emotional feedback. In this paper, we introduce Personalized IEA (PIEA) and present the RAPPIE model, which addresses subjective variability by incorporating reader feedback. In particular, (1) we create reader agents based on large language models to simulate reader feedback, overcoming the issue of ``spiral of silence effect'' and data incompleteness of real reader reaction. (2) We develop a role-aware multi-view graph learning to model the emotion interactive propagation process in scenarios with sparse reader information. (3) We construct two new PIEA datasets covering English and Chinese social media with detailed user metadata, addressing the text-centric limitation of existing datasets. Extensive experiments show that RAPPIE significantly outperforms state-of-the-art baselines, demonstrating the value of incorporating reader feedback in PIEA.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。