Multi-document Influence on Readers: Augmenting Social Emotion Prediction by Learning Document Interactions

Social emotion prediction aims to predict readers’ emotion, for example, emotion distributions evoked by documents (e.g., news articles). It makes a significant contribution to social media applications, such as opinion summary, election prediction, and emotions investigation of society. While recent studies have focused on encoding consecutive word sequences in documents using neural network models and leveraging topical information, it is essential to acknowledge the influence of documents sharing similar topics or being related to similar events on evoking readers’ emotions. The interactions among documents can significantly impact social emotion prediction. In this paper, we propose a novel approach to model the interactions among documents by constructing a heterogeneous graph. This graph captures the interaction among documents based on global word co-occurrence patterns in a corpus and the emotional scores of words obtained from emotion lexicons. Additionally, we develop heterogeneous graph convolution attention network (HGCA) to embed the heterogeneous graph. This network effectively captures the importance of different neighboring nodes and different node types, enabling comprehensive emotion prediction. Furthermore, we develop Taylor series expansion-based Transformer (Tayformer) to derive initialized node representations that can be co-trained with our graph network while having low memory complexity. Experimental results on four benchmark datasets show the effectiveness of our method.