Aspect-based sentiment analysis with semantic and syntactic enhanced multi-layer fusion model

Abstract

Aspect-based sentiment analysis (ABSA) aims to identify the sentiment polarity of specific aspect words or phrases in a sentence. Although recent studies have used attention mechanisms or syntactic relations of dependency trees to establish links between aspect terms and sentences, these approaches are imperfect in effectively fusing syntactic and semantic contextual information. Therefore, in this paper, we propose a novel multi-layer fusion model (MLFM) based on artificial intelligence (AI) techniques to efficiently fuse semantic and syntactic information for sentiment analysis. In the model, we first propose a new bi-graph convolutional network module for aspect term-centered aspect nodal attention (Aspect-NA) to enhance Semantic and Syntactic learning. Within Aspect-NA, we introduce dependency embedding and propose a dual embedding update mechanism that pays more attention to the influence of dependency types and semantics. In addition, we propose an adaptive hierarchical cross-attention (AHCA) for fusing the semantic information of aspect term with their associated syntactic features. AHCA not only effectively fuses features between syntax and semantics of the context, but also carries out the key features. We conducted experiments on six benchmark datasets, and the results show that our proposed model outperforms most baseline methods. The code and datasets involved in this paper are provided on https://github.com/jims-bug/MLFM.git.