Graph Convolutional Network with Interactive Memory Fusion for Aspect-based Sentiment Analysis

Abstract

Aspect-based sentiment analysis (ABSA) aims to predict the sentiment polarity of a specified aspect in a sentence. Graph neural networks (GNN) based on dependency trees have been shown to be effective for ABSA by explicitly modeling the connection between aspect and opinion terms and exploiting local semantic and syntactic information in the sentence. However, most previous works have overlooked the use of global dependency information. In this paper, we propose a novel Graph Convolutional Network (GCN) with an Interactive Memory Fusion (IMF) mechanism (IMF-GCN) that incorporates both global and local structural information for aspect-based sentiment classification. The IMF mechanism efficiently fuses global and local structural dependency information by assigning different weights to global and local dependency modules. Syntactic constraints are also imposed to prevent the graph convolution propagation unrelated to the target words, further improving the model’s performance. The evaluation metrics used in the paper are accuracy and macro-average F1 scores, and the proposed approach achieves optimal results on three datasets with F1 scores of 79.60%, 82.19%, and 77.75%, which outperform the baseline model.