Multi-Information Enhanced Graph Convolutional Network For Aspect Sentiment Analysis

Aspect level sentiment analysis aims to predict the emotional polarity of specific aspects of a sentence.However, there is still the problem of insufficient use of semantic information in the current stage of research, on the one hand, most of the existing work focuses on learning the dependency information between contextual words and aspect words, and does not make full use of the semantic information of sentences; on the other hand, the existing research does not focus on the syntax construction of dependency trees, so it does not make full use of the grammatical structure information to supplement the semantic information. In view of the above problems, this paper proposes a multi-information augmented graph convolutional neural network (MIE-GCN) model. It mainly includes two parts: one is to form a multi-information fusion layer through aspect perception attention, self-attention and external common sense to make full use of semantic information; the second is to construct a grammatical mask matrix of sentences according to the different grammatical distances between words, and supplement semantic information by obtaining comprehensive grammatical structure information. Finally, the graph convolutional neural network is used to enhance the node representation. The experimental results on the benchmark dataset show that the proposed model has a certain improvement over the comparison model. © 2024 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.