Embedding Extra Knowledge and A Dependency Tree Based on A Graph Attention Network for Aspect-based Sentiment Analysis

Aspect-based sentiment analysis analyses the fine-grained sentiment polarity of a particular attribute in a sentence. In addition to the fact that most papers have applied attention mechanisms and neural networks to fine-grained sentiment analysis, some studies have reported outstanding performance from graph-structured neural networks in aspect-based sentiment analysis. In this paper, we propose the use of a graph attention network (GAT) to embed external knowledge and grammatical relations (KD-GAT). First, we employ a GAT to extract the nodes and edges related to sentences in the knowledge graph. Second, we consider the influence of conjunctions and parts of speech based on the aspect-oriented syntactic dependency tree. Finally, we emphasize the effect of aspect position in the mechanism of multiple attention. Experiments on the SemEval 2014 and Twitter datasets show that our model can improve the ability to analyze sentiment and that the graph structure method can better integrate grammatical relations to understand a sentence.