CRC-MRC: Reader Comments Augmented Machine Reading Comprehension for social emotion prediction

Abstract

The task of social emotion prediction aims to understand and predict the distribution of emotion that a text evokes in its readers. Previous research has primarily focused on modeling the textual representation of news while neglecting the human aspect of how news is read and the emotions it evokes. Thus, we utilize the Machine Reading Comprehension (MRC) framework to read articles like humans do. Additionally, previous studies have shown the significant help of integrating readers’ comments. However, in realistic scenarios, raw comments are not readily available and are often redundant and noisy. To address this, we suggest a clustering-based approach that utilizes LLMs for the automatic generation of comments, aiming to provide high-quality emotional information from the reader’s perspective. By integrating generated comments into the MRC framework, we propose a Clustering-based Reader Comments Augmented Machine Reading Comprehension framework (CRC-MRC) to comprehensively model the reading process from the readers’ perspective while browsing news and comments. Extensive tests on benchmark datasets demonstrate the high effectiveness of our proposed framework, surpassing current state-of-the-art methods.