TS-GCN: Aspect-level sentiment classification model for consumer reviews

The goal of aspect-level sentiment classification (ASC) task is to obtain the
 sentiment polarity of aspect words in the text. Most existing methods ignore
 the implicit aspects, resulting in low classification accuracy. To improve
 the accuracy, this paper proposes a classification model for consumer
 reviews, abbreviated as TS-GCN (Truncated history attention and Selective
 transformation network-Graph Convolutional Networks). TS-GCN can classify
 sentiment from both explicit and implicit aspects. Firstly, we process the
 text by the BERT model and the BiLSTM model to obtain the text features.
 Secondly, the GCN model completes explicit sentiment classification by
 training text features. Due to the lack of implicit words, the GCN model
 cannot classify implicit sentiments. Finally, we predict implicit words
 based on the TS model, which makes up for the deficiency of the GCN model and
 completes the sentiment classification of implicit words. TS-GCN is proved
 on several datasets in the consumer reviews field. The results of
 experiments show that the TS-GCN can improve the accuracy and F1 of ASC.