Abstract: The goal of aspect-based sentiment analysis is to identify the emotional polarity of specific aspect words in a sentence.In recent years,many studies have utilized syntactic dependency relationships and self-attention mechanisms to obtain syntactic and semantic knowledge respectively,and updated representations by fusing these two types of information through graph convolutional networks.However,syntactic dependency relationships and self-attention mechanisms are not specific tools for sentiment analysis,and cannot directly and effectively capture the emotional expression of aspect words,which is the key to aspect-based sentiment analysis.In order to pay more attention to the emotional expression of aspect words,this paper constructs a network integrating syntax,semantics,and affective knowledge.Specifically,utilizing the syntactic knowledge in the syntactic dependency tree to construct a syntactic graph,and integrating external emotional knowledge information into the syntactic graph.At the same time,self-attention mechanism is adopted to obtain semantic knowledge of each word in the sentence,and aspect-aware attention mechanism is used to make the semantic graph focus on information related to aspect words.In addition,a bidirectional message propagation mechanism is used to learn the information in the two graphs at the same time and update node representations.The experimental results on three benchmark datasets validates the effectiveness of the proposed model.

Key words: Aspect-based sentiment analysis, Graph convolution networks, Attention mechanism, Syntax dependency tree, Affective knowledge, Natural language processing, Deep learning