In implicit emotion analysis (IEA), the subtlety of emotional expressions makes it particularly sensitive to user-specific characteristics. Existing studies often inject personalization into the analysis by focusing on the authorial dimension of the emotional text. However, these methods overlook the potential influence of the intended reader on the reaction of implicit emotions. In this paper, we refine the IEA task to Personalized Implicit Emotion Analysis (PIEA) and introduce the RAPPIE model, a novel framework designed to address the issue of missing user information within this task. In particular, 1) we create reader agents based on the Large Language Model to simulate reader reactions, to address challenges of the spiral of silence and data incompleteness encountered when acquiring reader feedback information. 2) We establish a reader propagation role system and develop a role-aware emotion propagation multi-view graph learning model, which effectively deals with the sparsity of reader information by utilizing the distribution of propagation roles. 3) We annotate two Chinese PIEA datasets with detailed user metadata, thereby addressing the limitation of prior datasets that primarily focus on textual content annotation. Extensive experiments on these datasets indicate that the RAPPIE model outperforms current state-of-the-art baselines, highlighting the significance and efficacy of incorporating reader feedback into the PIEA process.

翻译：在隐式情感分析任务中，情感表达的微妙性使其对用户特定特征尤为敏感。现有研究通常通过关注情感文本的作者维度来注入个性化信息。然而，这些方法忽视了预期读者对隐式情感反应可能产生的影响。本文中，我们将隐式情感分析任务细化为个性化隐式情感分析，并提出了RAPPIE模型——一个为解决该任务中用户信息缺失问题而设计的新型框架。具体而言：1）我们基于大语言模型构建读者代理以模拟读者反应，从而应对获取读者反馈信息时遇到的沉默螺旋和数据不完整性挑战；2）我们建立了读者传播角色体系，并开发了一种角色感知的情感传播多视图图学习模型，该模型通过利用传播角色的分布有效处理读者信息的稀疏性问题；3）我们标注了两个包含详细用户元数据的中文个性化隐式情感分析数据集，从而弥补了先前数据集主要集中于文本内容标注的局限性。在这些数据集上的大量实验表明，RAPPIE模型优于当前最先进的基线方法，凸显了将读者反馈纳入个性化隐式情感分析过程的重要性和有效性。