Interactive Double Graph Convolutional Networks for Aspect-based Sentiment Analysis

Abstract

Aspect-based sentiment analysis aims to judge the sentiment polarity of specific aspects in comments. Recent meth-ods use graph neural networks based on dependency trees to obtain the relationship between aspects and opinion words by using syntactic information. However, these models ignore the situation that the results of dependency tree parsing are incorrect and the sentences without significant syntactic structure. To solve these problems, in this paper, we propose an interactive double graph convolution networks (Inter-DGCN) model. We reconstruct the dependency tree according to the syntactic in-formation and construct the syntactic graph convolution module. Moreover, we construct the semantic graph convolution module, which uses multi-head self-attention to represent the semantic correlation between words. In addition, we add interactive feature fusion to learn the dual graph convolutional network interactively. Experimental results on three datasets demonstrate the effectiveness and advancement of our proposed model.