A Study of Dependency Graph Convolutional Networks Enhanced Sentiment Support Words for Aspect‐Level Sentiment Analysis

ABSTRACT The integration of syntactic and semantic features for aspect‐level sentiment analysis has emerged as a prominent research trend in recent years, typically achieved by combining syntactic dependency trees with graph convolutional networks. However, most existing methods can only extract a single class of syntactic and semantic features. This study identifies sentiment‐supporting words as a means to extract a richer set of syntactic and semantic features. Accordingly, this paper introduces a sentiment support word‐based dependency graph convolutional network (SSW‐RDGCN) model. This model generates matrices by leveraging the dependency relationships and distance relationships between the nodes of the syntactic dependency tree. These matrices are then processed through specific functions to create two new matrices, which, along with the initial features of the sentence, are input into a graph convolutional network. Subsequently, three types of syntactic and semantic features extracted from the sentiment support words are incorporated, and the aspect word features and sentiment support word features are augmented. Experiments conducted on three publicly available datasets demonstrate that the proposed model outperforms the baseline model.