EKIGCN: External Knowledge Injected Graph Convolutional Networks for Aspect-based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is a challenging subtask in the natural language processing community, which aims to determine the sentiment polarity about the corresponding specific aspect terms. In existing works, external knowledge has been proven effective to improve the ABSA tasks’ performance. However, it is still essential to study further to find a universal method for ABSA tasks. Therefore, we propose an external knowledge-injected graph convolutional network (EKIGCN) to enhance the sentiment representation with commonsense. Specifically, Sentic Net is leveraged to provide external knowledge to construct an affective dependency graph. In order to fully exploit the effectiveness of affective knowledge, a shallow mutual interaction is utilized to fuse the learned contextual representation and the graph. Besides, to make EKIGCN sensitive to aspect terms, an aspect-aware multi-head attention module is also employed to enhance the corresponding relations between aspect and contextual words. Extensive experiments are conducted on three popular datasets to validate the effectiveness of our proposed model, and the results outperform the mentioned state-of-the-art methods in ABSA tasks.