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

TL;DR

This paper introduces RAPPIE, a novel reader agent-based model for personalized implicit emotion analysis that incorporates simulated reader feedback and multi-view graph learning, improving emotion detection accuracy.

Contribution

The study presents the RAPPIE model, integrating reader feedback via large language model-based agents and multi-view graph learning, and creates new multilingual datasets for personalized emotion analysis.

Findings

RAPPIE outperforms existing baselines in emotion analysis tasks.

Simulated reader feedback enhances model personalization and accuracy.

New datasets in English and Chinese facilitate future research.

Abstract

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.