Improved Representations for Personalized Document-Level Sentiment Classification

Abstract

Incorporating personalization into document-level sentiment classification has gained considerable attention due to its better performance on diverse domains. Current progress in this field is attributed to the developed mechanisms of effectively modeling the interaction among the three fundamental factors: users, items, and words. However, how to improve the representation learning of the three factors themselves is largely unexplored. To bridge this gap, we propose to enrich users, items, and words representations in the state-of-the-art personalized sentiment classification model with an end-to-end training fashion. Specifically, relations between users and items are respectively modeled by graph neural networks to enhance original user and item representations. We further promote word representation by utilizing powerful pre-trained language models. Comprehensive experiments on several public and widely-used datasets demonstrate the superiority of the proposed approach, validating the contribution of the improved representations.