计算机工程与应用2024，Vol.60Issue(14)：144-151,8.DOI:10.3778/j.issn.1002-8331.2305-0376
用于方面级情感分析的多信息增强图卷积网络
Multi-Information Enhanced Graph Convolutional Network For Aspect Sentiment Analysis
摘要
Abstract
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 infor-mation.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.关键词
方面级情感分析/外部常识/方面感知注意力/语法掩码矩阵Key words
aspect-based sentiment analysis/external common sense/aspect perception attention/syntactic mask matrix分类
信息技术与安全科学引用本文复制引用
杨春霞,闫晗,吴亚雷,黄昱锟..用于方面级情感分析的多信息增强图卷积网络[J].计算机工程与应用,2024,60(14):144-151,8.基金项目
国家自然科学基金(61273229,51705260). （61273229,51705260）
