An adaptive Dual Graph Convolution Fusion Network for Aspect-Based Sentiment Analysis

Abstract

Aspect-based Sentiment Analysis (ABSA), also known as fine-grained sentiment analysis, aims to predict the sentiment polarity of specific aspect words in the sentence. Some studies have explored the semantic correlation between words in sentences through attention-based methods. Other studies have learned syntactic knowledge by using graph convolution networks to introduce dependency relations. These methods have achieved satisfactory results in the ABSA tasks. However, due to the complexity of language, effectively capturing semantic and syntactic knowledge remains a challenging research question. Therefore, we propose an Adaptive Dual Graph Convolution Fusion Network (AD-GCFN) for aspect-based sentiment analysis. This model uses two graph convolution networks: one for the semantic layer to learn semantic correlations by an attention mechanism, and the other for the syntactic layer to learn syntactic structure by dependency parsing. To reduce the noise caused by the attention mechanism, we designed a module that dynamically updates the graph structure information for adaptively aggregating node information. To effectively fuse semantic and syntactic information, we propose a cross-fusion module that uses the double random similarity matrix to obtain the syntactic features in the semantic space and the semantic features in the syntactic space, respectively. Additionally, we employ two regularizers to further improve the ability to capture semantic correlations. The orthogonal regularizer encourages the semantic layer to learn word semantics without overlap, while the differential regularizer encourages the semantic and syntactic layers to learn different parts. Finally, the experimental results on three benchmark datasets show that the AD-GCFN model is superior to the contrast models in terms of accuracy and macro-F1.