计算机科学

计算机科学 ›› 2024, Vol. 51 ›› Issue (6): 299-308.doi: 10.11896/jsjkx.230600059

• 人工智能 • 上一篇    下一篇

基于句信息增强词信息的方面级情感分类

李怡霖1, 孙成胜2, 罗林3, 琚生根1   

  1. 1 四川大学计算机学院 成都 610065
    2 中国电子科技网络信息安全有限责任公司 成都 610041
    3 中国电子科技集团公司第三十研究所 成都 610041
  • 收稿日期:2023-06-07 修回日期:2023-11-25 出版日期:2024-06-15 发布日期:2024-06-05
  • 通讯作者: 琚生根(jsg@scu.edu.cn)
  • 作者简介:(lyl_scu170@163.com)
  • 基金资助:
    国家自然科学基金(62137001)

Aspect-based Sentiment Classification for Word Information Enhancement Based on Sentence Information

LI Yilin1, SUN Chengsheng2, LUO Lin3, JU Shenggen1   

  1. 1 College of Computer Science,Sichuan University,Chengdu 610065,China
    2 China Electronic Technology Cyber Security Co.,Ltd,Chengdu 610041,China
    3 No.30 Research Institute of CETC,Chengdu 610041,China
  • Received:2023-06-07 Revised:2023-11-25 Online:2024-06-15 Published:2024-06-05
  • About author:LI Yilin,born in 1997,postgraduate,is a member of CCF(No.K9226G).His main research interests include natural language processing and sentiment analysis.
    JU Shenggen,born in 1970,Ph.D,professor,Ph.D supervisor,is a senior member of CCF(No.14364S).His main research interests include data mining,natural language processing and know-ledge graph.
  • Supported by:
    National Natural Science Foundation of China(62137001).

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摘要： 方面级情感分类属于细粒度的情感分类,旨在判断句子中指定方面词的情感极性。近年来,句法知识在方面级情感分类任务中得到了广泛应用。目前主流的模型利用句法依存树和图卷积神经网络进行情感极性的分类。然而,此类模型着眼于利用聚合后的方面词信息来判断情感极性,很少关注句子的全局信息对情感极性的影响,从而导致情感极性分类结果出现偏差。为了解决这一问题,提出了一种基于句信息增强词信息的方面级情感分类模型,该模型通过对比学习对句向量进行表示学习,以减小句向量对比损失为目标调整词向量的特征表示,最后通过图卷积神经网络聚合意见词信息得出情感分类结果。在SemEval2014数据集和Twitter数据集上进行实验,结果表明,所提模型可以提高分类的准确性,综合验证了该方法的有效性。

关键词: 方面级情感分类, 句信息, 词信息, 对比学习, 图卷积神经网络

Abstract: Aspect-based sentiment classification is a fine-grained sentiment classification task that aims to determine the sentiment polarity of specified aspect terms in a sentence.In recent years,syntactic knowledge has been widely applied in the field of aspect-based sentiment classification.Current mainstream models utilize syntactic dependency trees and graph convolutional neural networks to classify sentiment polarity.However,these models primarily focus on using aggregated aspect term information to determine sentiment polarity,and few studies focus on the impact of global sentence information on sentiment polarity.This leads to biased sentiment classification results.To address this issue,this paper proposes an aspect-based sentiment classification model that enhances aspect term information with sentence-level information.This model learns sentence representations through con-trastive learning,with the goal of minimizing the contrastive loss of sentence vectors to adjust the feature representation of word vectors.Finally,the model aggregates opinion word information using a graph convolutional neural network(GCN)to obtain sentiment classification results.Experimental results on the SemEval2014 dataset and Twitter dataset demonstrate that the model improves classification accuracy,which verifies the effectiveness of our approach.

Key words: Aspect-based sentiment classification, Sentence information, Word information, Contrastive learning, Graph convolutional network

中图分类号: 

  • TP391
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