Affection Enhanced Dual Graph Convolution Network for Aspect Based Sentiment Analysis

Abstract

Aspect-based sentiment analysis is a fine-grained sentiment classification task. In recent years, graph neural network on dependency tree has been used to model the dependency relationship between aspect terms and their opinion terms. However, such methods usually have the disadvantage of highly dependent on the quality of dependency parsing. Furthermore, most existing works focus on syntactic information, while ignoring the effect of affective knowledge in modeling the sentiment-related dependencies between specific aspects and context. In order to solve these problems, an affection enhanced dual graph convolution network is designed and proposed for aspect-based sentiment analysis. The model establishes a dual channel structure based on the dependency tree and attention mechanism, which can more accurately and efficiently capture the syntactic and semantic dependencies between aspects and contexts, and reduce the dependence of the model on the dependency tree. In addition, affective knowledge is integrated to enhance the graph structure and help the model better extract the sentiment-related dependencies of specific aspects. The accuracy of the model on the three open benchmark datasets Rest14, Lap14 and Twitter reaches 84.32%, 78.20% and 76.12% respectively, approaching or exceeding the state-of-the-art perfor-mance. Experiments show that the method proposed can make rational use of semantic and syntactic information, and achieves advanced sentiment classification performance with fewer parameters.

Keywords

• aspect-based sentiment analysis; attention mechanism; dependency tree; graph convolutional networks; affective knowledge