Aspect-based Sentiment Analysis with Graph Convolutional Networks over Dependency Awareness

Aspect-based sentiment analysis (ABSA) aims to predict the sentiment polarities of specific aspects in a comment sentence. Nowadays, models based on graph neural networks enhance semantic perception by using dependency relations on dependency graphs to analyze context and aspect words. However, these models ignore the importance of the dependency type information contained in word relations and do not utilize the dependency types to pay attention to semantic information and the noise problem caused by dependency tree parsing error. To solve the above problems, we propose a novel deep dependency-aware graph convolutional networks (DA-GCN) model in this paper. Among them, the DA-GCN establishes interactive relations with multi-head attention and makes use of the grammar information of dependency perception jointly to effectively learn related information from the generated graphs. We introduce multiple conditional random fields fusing structured attention to better capture specific aspect opinion words. Experimental results on five datasets prove the effectiveness and advancement of our proposed model.