A Novel Bi-Branch Graph Convolutional Neural Network for Aspect Level Sentiment Classification

Abstract

Aspect-level sentiment classification is a fine-grained task in sentiment analysis whose main purpose is to identify the sentiment polarity of a specific aspect. Current Graph Convolutional Network (GCN) has its distinctive superiority in tackling sentiment classification both semantically and syntactically. However, GCN still has deficiencies in introducing the noise during processing and dealing with sentences of complex structure. To address these issues, we propose a novel Bi-branch GCN (Bi-B GCN). In our model, an attention weight graph, by employing the attention mechanism, is constructed to substitute the basic syntax dependency tree and thus to remove the irrelevant information. Furthermore, a semantic dependency graph is devised to supplement the semantic information to the syntax dependency tree, based on which the connection between different words can be captured. In addition, on the task of sentiment classification, the integration of semantic information and the syntactic information is conducted by using a combinational gated mechanism. Substantial experiments to validate the working performance of Bi-B GCN are performed on a variety of datasets. The encouraging results establish a strong evidence of the high accuracy of the proposed model.