Integrating external knowledge into aspect-based sentiment analysis using graph neural network

Aspect-based sentiment analysis aims to analyze the sentiment polarity of a given aspect. The graph convolutional neural network model is widely used. However, most existing research focuses on mining the context-word-to-aspect-word dependencies of dependency trees based on the sentence itself without using much text-related external knowledge. In addition, the problem of reasonably capturing words outside the multihop grammatical distance and edge label hinders the effect of GCN. This paper proposes a graph convolutional network that fuses external knowledge (sentiment lexicon and part-of-speech information) (EK-GCN). Specifically, we conduct a statistical study on part-of-speech and construct a part-of-speech matrix to fully consider the influence of denying words, degree words, and other words that affect sentiment expression in sentences on sentiment classification. Then, an external sentiment lexicon is used to assign sentiment scores to each word in the sentence to construct a sentiment score matrix to highlight the weight of sentiment words, which to a certain extent, compensates for the fact that the syntactic dependency tree cannot capture edge labels. In addition, we design a Word–Sentence Interaction Network (WSIN), which can fully consider the information of the current aspect word and interact with the context information of the reviews to filter useful sentence information. We conduct experiments on four benchmark datasets, and the excellent experimental results demonstrate the effectiveness of our model. The results also verify that fully integrating external knowledge can assist in completing aspect-based sentiment analysis tasks.