Highway-Based Local Graph Convolution Network for Aspect Based Sentiment Analysis

Aspect-level sentiment analysis is a fine-grained task in sentiment analysis, whose target is to identify the sentiment polarity of a specific aspect in a sentence. Due to the complexity of the human language, the widely-applied syntactic-based neural network methods have deficiencies in precisely capturing the relation between aspects and opinion words, and thus results in the misunderstanding of the sentiment. To address such issue, we focus on optimizing the encoding of syntactic information. To start with, the sub-dependency trees, from the basic dependency tree, are constructed in line with the syntactic distance. Further, we propose a novel Highway-Based Local Graph Convolution Network (HL-GCN) to capture the more-related information and thus facilitate the sentiment classification. Substantial experiments on a variety of datasets are performed. Comparing to the state-of-arts, the proposed model shows the effectiveness in eliminating the noise from the dependency tree, which results in an even higher classification accuracy.