Twain-GCN: twain-syntax graph convolutional networks for aspect-based sentiment analysis

The goal of aspect-based sentiment analysis is to recognize the aspect information in the text and the corresponding sentiment polarity. A variety of robust methods, including attention mechanisms and convolutional neural networks, have been extensively utilized to tackle this complex task. Better experimental results are obtained by using graph convolutional networks (GCN) based on semantic dependency trees in previous studies. Therefore, abundant methods begin to use sentence structure information to complete this task. However, only the loose connection between aspect words and contexts is realized in some practices due to sentences may contain complex relations. To solve this problem, Twain-Syntax graph convolutional network model is proposed, which can utilize multiple syntactic structure information simultaneously. Guided by the constituent tree and dependency tree, rich syntactic information is fully used in the model to build the sentiment-aware context for each aspect. In special, the multilayer attention mechanism and GCN are employed for learning to capture the correlation between words. By integrating syntactic information, this approach significantly refines the model’s technical performance. Extensive testing on four benchmark datasets shows that the model delineated in this paper exhibits high levels of efficiency, comparable to several cutting-edge models.