计算机科学 ›› 2025, Vol. 52 ›› Issue (7): 218-225.doi: 10.11896/jsjkx.240500124

• 人工智能 • 上一篇    下一篇

基于句法、语义和情感知识的方面级情感分析

郑诚, 杨楠   

  1. 安徽大学计算机科学与技术学院 合肥 230601
    计算智能与信号处理教育部重点实验室(安徽大学) 合肥 230601
  • 收稿日期:2024-05-28 修回日期:2024-10-18 发布日期:2025-07-17
  • 通讯作者: 郑诚(csahu@126.com)
  • 基金资助:
    安徽省重点研发计划(202004d07020009)

Aspect-based Sentiment Analysis Based on Syntax,Semantics and Affective Knowledge

ZHENG Cheng, YANG Nan   

  1. School of Computer Science and Technology, Anhui University, Hefei 230601, China
    Key Laboratory of Intelligent Computing & Signal Processing(Anhui University), Ministry of Education, Hefei 230601, China
  • Received:2024-05-28 Revised:2024-10-18 Published:2025-07-17
  • About author:ZHENG Cheng,born in 1964,Ph.D,associate professor.His main research interests include data mining and text analysis,natural language processing.
  • Supported by:
    Key Research and Development Program of Anhui Province(202004d07020009).

摘要: 方面级情感分析的目标是识别句子中特定方面词的情感极性。近年来,许多工作都是利用句法依赖关系和自注意力机制分别获得句法知识和语义知识,并通过图卷积网络融合这两种信息更新节点的表示。然而句法依赖关系和自注意力机制都不是特定用于情感分析的工具,不能直接有效地捕获方面词的情感表达,而这一点正是方面级情感分析的关键之处。为了更准确地识别方面词的情感表达,构造了融合句法、语义和情感知识的网络。具体来说,利用句法依赖树中的句法知识构建句法图,并将外部情感知识库信息融合在句法图中。同时,采用自注意力机制获得句子中各单词的语义知识,并通过方面感知注意力机制使语义图关注与方面词相关的信息。此外,采用双向消息传播机制同时学习这两个图中的信息并更新节点表示。在3个基准数据集上的实验结果验证了所提模型的有效性。

关键词: 方面级情感分析, 图卷积网络, 注意力机制, 句法依赖树, 情感知识, 自然语言处理, 深度学习

Abstract: The goal of aspect-based sentiment analysis is to identify the emotional polarity of specific aspect words in a sentence.In recent years,many studies have utilized syntactic dependency relationships and self-attention mechanisms to obtain syntactic and semantic knowledge respectively,and updated representations by fusing these two types of information through graph convolutional networks.However,syntactic dependency relationships and self-attention mechanisms are not specific tools for sentiment analysis,and cannot directly and effectively capture the emotional expression of aspect words,which is the key to aspect-based sentiment analysis.In order to pay more attention to the emotional expression of aspect words,this paper constructs a network integrating syntax,semantics,and affective knowledge.Specifically,utilizing the syntactic knowledge in the syntactic dependency tree to construct a syntactic graph,and integrating external emotional knowledge information into the syntactic graph.At the same time,self-attention mechanism is adopted to obtain semantic knowledge of each word in the sentence,and aspect-aware attention mechanism is used to make the semantic graph focus on information related to aspect words.In addition,a bidirectional message propagation mechanism is used to learn the information in the two graphs at the same time and update node representations.The experimental results on three benchmark datasets validates the effectiveness of the proposed model.

Key words: Aspect-based sentiment analysis, Graph convolution networks, Attention mechanism, Syntax dependency tree, Affective knowledge, Natural language processing, Deep learning

中图分类号: 

  • TP391
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