考虑读者反馈，提升个性化隐含情绪分析效果
My Words Imply Your Opinion: Reader Agent-based Propagation Enhancement for Personalized Implicit Emotion Analysis
- 用大模型模拟读者角色，生成反馈以弥补真实数据缺失
- 在读者信息稀疏时仍能有效捕捉情绪传播过程
- 构建中英文双语数据集，支持更真实的个性化分析
隐含情绪分析（IEA）因情感表达微妙，对用户特征敏感。现有研究仅关注作者视角，忽视目标读者对情绪反馈的影响。本文提出个性化隐含情绪分析（PIEA）框架，并设计RAPPIE模型，通过引入读者代理来建模读者反馈，克服真实反馈数据不完整与‘沉默螺旋’效应问题。具体包括：(1) 基于大语言模型构建读者代理，模拟读者反应；(2) 设计角色感知的多视图图学习机制，建模稀疏读者信息下的情绪传播；(3) 构建两个包含中英文社交媒体数据的新数据集，涵盖详细用户元信息，突破现有数据集以文本为中心的局限。大量实验表明，RAPPIE显著优于现有先进基线，验证了引入读者反馈在个性化分析中的价值。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。