计算机科学

计算机科学 ›› 2023, Vol. 50 ›› Issue (5): 230-237.doi: 10.11896/jsjkx.220300008

• 人工智能 • 上一篇    下一篇

基于情感知识的双通道图卷积网络的方面级情感分析

阳影1, 张凡1,2, 李天瑞1,2,3   

  1. 1 西南交通大学计算机与人工智能学院 成都 611756
    2 四川省制造业产业链协同与信息化支撑技术重点实验室 成都 611756
    3 综合交通大数据应用技术国家工程实验室 成都 611756
  • 收稿日期:2022-03-01 修回日期:2022-09-01 出版日期:2023-05-15 发布日期:2023-05-06
  • 通讯作者: 李天瑞(trli@swjtu.edu.cn)
  • 作者简介:(G_yangying@163.com)
  • 基金资助:
    国家自然科学基金(62176221)

Aspect-based Sentiment Analysis Based on Dual-channel Graph Convolutional Network with Sentiment Knowledge

YANG Ying1, ZHANG Fan1,2, LI Tianrui1,2,3   

  1. 1 School of Computing and Artificial Intelligence,Southwest Jiaotong University,Chengdu 611756,China
    2 Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province,Chengdu 611756,China
    3 National Engineering Laboratory of Integrated Transportation Big Data Application Technology,Chengdu 611756,China
  • Received:2022-03-01 Revised:2022-09-01 Online:2023-05-15 Published:2023-05-06
  • About author:YANG Ying,born in 1997,postgra-duate,is a member of China Computer Federation.Her main research interests include sentiment analysis and natural language processing.
    LI Tianrui,born in 1969,Ph.D,professor,Ph.D supervisor,is a distinguished member of China Computer Federation.His main research interests include big data intelligence,rough sets,granular computing.
  • Supported by:
    National Natural Science Foundation of China(62176221).

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摘要： 方面级情感分析是一项细粒度情感分析任务,其目标是对句子中给定的方面词进行情感极性分类。当前的情感分类模型大多在依存句法树上构建图神经网络,从依存句法树上学习方面词与上下文之间的信息,缺乏对句子中情感知识的挖掘。针对这个问题,文中提出了一种基于情感知识的双通道图卷积网络的情感分类模型(Dual-channel Graph Convolutional Network with Sentiment Knowledge,SKDGCN)。该模型由情感增强的依存图卷积网络(Sentiment-enhanced Dependency Graph Convolutional Network,SDGCN)和注意力图卷积网络(Attention Graph Convolutional Network,AGCN)组成,两个图卷积网络分别学习方面词与上下文词的句法依赖关系和语义关系。具体地,SDGCN在句法依存树上融合SenticNet中的情感知识以增强句子的依赖关系,使得模型既考虑了上下文词与方面词的句法关系,也考虑了上下文中意见词与方面词的情感信息;AGCN使用注意力机制学习方面词与句子中上下文的语义相关性;最后使两个图卷积网络交互学习各自的信息进行情感分类。实验结果表明,该模型在多个公开数据集上表现优异,并通过消融实验验证了各个模块的有效性。

关键词: 方面级情感分析, 情感知识, 依存关系, 图卷积网络, 注意力机制

Abstract: Aspect-based sentiment analysis is a fine-grained sentiment analysis task whose goal is to classify the sentiment polarity of the given aspect terms in a sentence.Most of the current sentiment classification models build a graph neural network on the dependency syntax tree,and learn the information between the aspect terms and the context from the dependency syntax tree,and lack the mining of sentiment knowledge in the sentence.To solve this problem,this paper proposes a sentiment classification model based on dual-channel graph convolutional network with sentiment knowledge.The model consists of a sentiment-enhanced dependency graph convolutional network(SDGCN) and an attention graph convolutional network(AGCN),which learn the syntactic dependencies and semantic relations of aspect terms and context words,respectively.Specifically,SDGCN incorporates sentiment knowledge from SenticNet on syntactic dependencies to enhance sentence dependencies,so that the model considers the syntactic relationship between context and aspects,together with the sentiment information between opinion words in the context and aspect terms.The attention mechanism is used by AGCN to learn the semantic relevance between aspect terms and the context in the sentence.Finally,the two graph convolution networks learn their own information interactively for sentiment classification.Experimental results show that the proposed model performs well on multiple public datasets,and ablation experiments verify the effectiveness of each module.

Key words: Aspect-based sentiment analysis, Sentiment knowledge, Dependencies, Graph convolutional networks, Attention mecha-nism

中图分类号: 

  • TP391
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