Multi-grained Syntactic Dependency-aware Graph Convolution for Aspect-based Sentiment Analysis

Abstract

Aspect-based sentiment analysis (ABSA) aims to identify the sentiment of one or more aspects in the text. Existing methods pay attention to the syntactic structure, and significant progress has been achieved by using graph convolutional network (GCN). However, they ignore internal connections between different types of syntactic structure from a fine-grained perspective, which may lead to underexploring critical syntactic information of sentences. Additionally, the aspect-oriented syntactic dependency is omitted that generally provides penetrating insights of the corresponding sentiment. To tackle these problems, we propose a multi-grained (both coarse-grained and fine-grained) syntactic dependency-aware graph convolutional network model (named MSD-GCN). Particularly, in the initial representation layer, we redesign the aspect-enhanced coarse-grained dependency graph and construct five fine-grained dependency graphs by taking into account the types of syntactic structure. Moreover, we explore a multi-grained syntactic enhancement layer, which employs GCN and attention mechanism over multi-grained dependency graphs to capture more abundant syntactic information. Experimental results on five datasets illustrate that our proposed MSD-GCN model outperforms other representative ones in terms of Accuracy and Macro-Averaged F1.