Aspect-Dependent Heterogeneous Graph Convolutional Network for Aspect-Level Sentiment Analysis

Aspect-based sentiment analysis aims to identify the sentiment polarity of a certain aspect in the context sentence. It only use the syntax dependency tree to construct graph convolution in previous sentiment analysis methods. But one aspect of sentiment sometimes only be determined by a few words, and relying entirely on the syntax tree may distract the model's attention. Also, due to the limitations of the corpus, the model is only able to learn limited knowledge. To address the above limitations, we propose a sentiment classification model based on aspect-dependent heterogeneous graph convolutional network (named ADHGCN). The model prunes the dependency tree, and then it can reduce the influence of featherweight information on the results. In addition, the model fuses multiple feature relationships between words by constructing a heterogeneous graph, and applies graph convolutional network (GCN) to seek meaningful representations for each node. It makes the model further integrate a variety of information on the original basis, and then it is no longer purely dependent on one relation. At the same time, this paper will introduce a common-sense knowledge base to participate in the construction of heterogeneous graphs, so that the model can learn knowledge beyond the corpus, thereby improving the accuracy of sentiment classification. Through experiments, we have demonstrated that our network performs better than others on five public datasets.