西华大学学报（自然科学版）2024，Vol.43Issue(3)：8-17,53,11.DOI:10.12198/j.issn.1673-159X.5256
面向方面级情感分析的交互式图卷积网络
Interactive Graph Convolutional Network for Aspect-level Sentiment Analysis
摘要
Abstract
A novel interactive graph convolutional network model is proposed to overcome the limita-tions of existing sentiment analysis models based on graph networks.This model effectively addresses the challenges of handling the internal semantic relations of aspect phrases and the sentiment interactions between different aspects within a sentence.Firstly,by leveraging the syntactic dependencies between con-text and aspect words,the aspect-inside module integrates the internal semantic correlations among aspect phrases and establishes sentiment interactions between different aspects,resulting in four types of adja-cency matrix graphs.This allows for accurate modeling of aspect phrase semantics and the affective interac-tions between different aspects within a sentence.Secondly,an aspect interaction graph is constructed to capture cross-aspect relations,effectively resolving the issue of sentiment interactions between different as-pects.Lastly,the inclusion of a global node in the graph convolutional network further addresses the prob-lem of fluctuating accuracy when multiple aspect words are present in a sentence.The results of experi-mental on four publicly available datasets demonstrate the effectiveness of the proposed model,showing significant improvements in both accuracy and F1 size.关键词
方面级情感分析/图卷积网络/短语结构树Key words
aspect-level sentiment analysis/graph convolutional networks/phrase structure tree分类
信息技术与安全科学引用本文复制引用
朱忆红,陈晓亮,付俊森,杜亚军..面向方面级情感分析的交互式图卷积网络[J].西华大学学报（自然科学版）,2024,43(3):8-17,53,11.基金项目
国家自然科学基金项目(61902324) （61902324）
四川省科技厅重点研发项目(2023YFS0424). （2023YFS0424）
