SS-GCN:Aspect-based Sentiment Analysis Model with Affective Enhancement and Syntactic Enhancement

Abstract

Aspect-based sentiment analysis(ABSA),as a downstream application of knowledge graph,belongs to the fine-grained sentiment analysis task,which aims to understand the sentiment polarity of people on the evaluation target at the aspect level.Relevant research in recent years has made significant progress,but existing methods focus on exploiting sequentiality or syntactic dependency constraints within sentences,and do not fully exploit the type of dependencies between context words and aspect words.In addition,the existing graph-based convolutional neural network models have insufficient ability to retain node features.In response to this problem,firstly,based on the syntactic dependency tree,this paper fully excavates the dependency types between context words and aspect words,and integrates them into the construction of the dependency graph.Second,we define a “sensitive relation set”,which is used to construct auxiliary sentences to enhance the correlation between specific context words and aspect words,and at the same time,combined with the sentiment knowledge network SenticNet to enhance the sentence dependency graph,and then improve the construction of the graph neural network.Finally,a context retention mechanism is introduced to reduce the information loss of node features in the multilayer graph convolution neural network.The proposed SS-GCN model fuses the syntactic and contextual representations learned in parallel to accomplish sentiment enhancement and syntactic enhancement,and extensive experiments on three public datasets demonstrate the effectiveness of SS-GCN.

Keywords

• aspect-level sentiment analysis|graph convolutional networks|senticnet|attention mechanism|bi-lstm