Dynamic and Multi-Channel Graph Convolutional Networks for Aspect-Based Sentiment Analysis

Abstract

Aspect-based sentiment analysis aims to judge the polarity of the given aspect word in reviews. Most recent methods adopt syntax-based Graph Neural Networks to extract the syntactic information from the dependency graph, thinking that would be beneficial for establishing relations between aspect and opinion words. However, these methods may ignore that some sentences have no remarkable syntactic structure, which causes the opposite judgement in sentiment analysis. In this paper, we figure out this problem by means of optimally fusing syntactic information, semantic information and their combinations simultaneously. Firstly, syntactic graphs and semantic graphs are generated by dependency tree and multi-head self-attention respectively. Then we propose a Dynamic and Multi-channel Graph Convolutional Network (DM-GCN) to learn the correlated information from the generated graphs effectively. Our extensive experiments on SemEval 2014 and Twitter datasets confirm that DM-GCN fuses syntactic, semantic and their combinations optimally and outperforms all state-of-the-art alternatives with a large margin.