Syntactic Structure-Enhanced Dual Graph Convolutional Network for Aspect-Level Sentiment Classification

Abstract

Aspect-level sentiment classification (ALSC) is a fine-grained sentiment analysis task that aims to predict the sentiment of the given aspect in a sentence. Recent studies mainly focus on using the Graph Convolutional Networks (GCN) to deal with both the semantics and the syntax of a sentence. However, the improvement is limited since the syntax dependency trees are not aspect-oriented and the exploitation of syntax structure information is inadequate. In this paper, we propose a Syntactic Structure-Enhanced Dual Graph Convolutional Network (SSEDGCN) model for an ALSC task. Firstly, to enhance the relation between aspect and its opinion words, we propose an aspect-wise dependency tree by reconstructing the basic syntax dependency tree. Then, we propose a syntax-aware GCN to encode the new tree. For semantics information learning, a semantic-aware GCN is established. In order to exploit syntactic structure information, we design a syntax-guided contrastive learning objective that makes the model aware of syntactic structure and improves the quality of the feature representation of the aspect. The experimental results on three benchmark datasets show that our model significantly outperforms the baseline models and verifies the effectiveness of our model.

Keywords

• aspect-level sentiment classification
• contrasitve learning
• graph convolutional networks