A Sentiment and Syntactic-Aware Graph Convolutional Network for Aspect-level Sentiment Classification

Aspect-level sentiment classification (ASC) is a significant problem in fine-grained sentiment analysis, which automatically predicts the sentiment polarity of a given aspect in a sentence. Dependency tree-based graph convolutional networks have been widely studied for their ability to effectively capture the dependencies of aspect words with other words. However, constructing more accurate syntactic trees by introducing external knowledge has limited improvement on ungrammatical informal texts and has led to over-parameterization of the model. To alleviate this problem, we propose a sentiment and syntactic-aware graph convolutional network (SaS-GCN) that combines syntactic and sentiment relations. We use an attention mechanism and the Sparsemax activation function to construct a sparse sentiment-dependent graph. Compared with existing methods that use LSTM or CNN to obtain semantics from text directly, this graph, combined with a GCN, contains more semantic features. Moreover, we redesign the network structure of GCN, calling it EN-GCN, to make it sensitive to node dimensional features and hence to have a strong feature mining ability. The experimental results indicate that our model outperforms state-of-the-art methods. In particular, when evaluated on the Rest15 and Rest16 datasets, the F1 scores of the proposed lightweight model are 4.15% and 3.77% better than BERT respectively.