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A model LSI-BERT based on BERT and fused lexical and syntactic information (LSI) is proposed to address the shortcomings of the existing end- to- end aspect-based sentiment analysis (E2E-ABSA) method research that does not fully utilize textual information. A BERT embedding layer and a TFM feature extractor are used to extract semantic information, and lexical information is extracted by the industrial- grade natural language processing tool SpaCy. Two weighting factors α and β are introduced to fuse semantic and lexical information. Graph attention networks (GAT) is used to extract syntactic dependency information based on the adjacency matrix generated from the syntactic dependency tree. A dual-stream attention network is used to fuse syntactic dependency information and textual information fused with lexical information to achieve better interaction between these two types of information. The experimental results show that the model outperforms the current representative model on three commonly used benchmark datasets. [ABSTRACT FROM AUTHOR]