MIFINN: A novel multi-information fusion and interaction neural network for aspect-based sentiment analysis

Aspect-based Sentiment Analysis (ABSA) is used to detect corresponding sentiment polarities toward different aspect terms. In recent years, graph convolutional network-based methods achieve great success for ABSA. However, previous studies ignore syntactic dependency types and aspect-related syntactic distances. Besides, effectively fusing semantic and syntactic information remains a challenging problem. Previous studies also neglect interactions between aspect terms and context. To alleviate the above problems, we develop a Multi-Information Fusion and Interaction Neural Network (MIFINN), which consists of an Aspect and Syntax-Aware Multi-Information Fusion Graph Convolutional Network (ASAMIFGCN) and a Multi-Information Interaction and Gating Network (MIIGN). The ASAMIFGCN model can integrate and fuse contextual semantics, syntactic dependency types, and aspect-related syntactic distances. Specifically, two different attentions are designed to learn global and aspect-related semantic and syntactic information, respectively. A syntactic distance gating module is proposed to acquire more precise score matrices. Besides, we propose a semantic and syntactic association module to fuse multi-information simultaneously. The MIIGN model can effectively interact aspect terms and context. Specifically, two interactive attentions are developed to learn different weights toward aspect terms and context via interactions between each other, respectively. Moreover, we propose a gating information module for controlling the information flow of aspect terms and context. We conduct experiments on multiple datasets and MIFINN achieves state-of-the-art performance. The results also demonstrate that effectively fusing and interacting multi-information can improve model performance for ABSA significantly.