Abstract: Aspect-based sentiment analysis is a fine-grained sentiment analysis task that aims to analyze the sentiment polarity of specific aspects in a given text.Current syntax-based methods heavily rely on a single parsing result from the dependency tree,and most of the existing research lacks sufficient integration of semantic and syntactic features.Therefore,this paper proposes an aspect-based sentiment analysis approach based on the BERT model and graph attention network.This method can effectively explo the semantic and syntactic information in sentence structures and fuse these information through an interactive attention mechanism to obtain more accurate sentiment features.Firstly,the BERT pre-trained model is utilized to obtain the initial vectors of the text,and an attention mechanism is employed to associate the globalsemantic information of aspect words,resulting in the semantic features of the text.Secondly,a syntax parser is used to construct a phrase structure graph and a dependency graph,and a graph attention network is applied to encode the node information,leading to the syntactic features of the text.Finally,through an interactive attention mechanism,the learned semantic and syntactic features are combined to achieve a comprehensive understanding of aspect-opinion relationships from multiple perspectives.Experimental results show that the proposed method outperforms the existing state-of-the-art methods with ACC and F1 values on multiple datasets.

Key words: Aspect-based sentiment analysis, Syntax analysis, Graph attention network, Attention mechanism