Triple-channel graph attention network for improving aspect-level sentiment analysis

Aspect-level sentiment classification is a fine-grained sentiment analysis that primarily focuses on predicting the sentiment polarity of aspects within a sentence. At present, many methods employ graph convolutional networks (GCN) to extract hidden semantic or syntactic information from sentences, achieving good results. However, these existing methods often overlook the relationships between multiple aspects within a sentence, treating aspects separately and thus neglecting the sentiment connections. To address this issue, this paper introduces a triple-channel graph attention network (TC-GAT) to capture semantics, syntax and multiple aspects dependencies information. In addition, a simple and effective fusion mechanism is proposed to comprehensively integrate these three types of information. Experiments are carried out on three commonly datasets, and the results verify the effectiveness of our proposed model.