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《山东大学学报(理学版)》 ›› 2021, Vol. 56 ›› Issue (11): 15-23.doi: 10.6040/j.issn.1671-9352.0.2021.425

• • 上一篇    

融合图卷积神经网络的文本情感分类

阴爱英1,3,林建洲2,3,吴运兵2,3*,廖祥文2,3   

  1. 1.福州大学 至诚学院计算机工程系, 福建 福州 350002;2.福州大学 计算机与大数据学院, 福建 福州 350108;3.数字福建金融大数据研究所, 福建 福州 350108
  • 发布日期:2021-11-15
  • 作者简介:阴爱英(1976— ),女,硕士,讲师,研究方向为数据挖掘、文本分析. E-mail:43547598@qq.com*通信作者简介:吴运兵(1976— ),男,硕士,副教授,研究方向为知识表示与知识发现. E-mail:wyb5820@fzu.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(61976054,61772135)

Sentiment classification combining graph convolution neural network

YIN Ai-ying1,3, LIN Jian-zhou2,3, WU Yun-bing2,3*, LIAO Xiang-wen2,3   

  1. 1. Department of Computer Engineering, Zhicheng College of Fuzhou University, Fuzhou 350002, Fujian, China;
    2.College of Computer and Big Data, Fuzhou University, Fuzhou 350108, Fujian, China;
    3. Digital Fujian Institute of Financial Big Data, Fuzhou 350108, Fujian, China
  • Published:2021-11-15

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摘要： 文档级别情感分类旨在预测用户对评论文本的情感极性标签。最近研究发现,利用用户和产品信息能有效地提升情感分类性能,然而,现有大多数研究只关注用户与评论、产品与评论的信息,忽略了用户与用户、产品与产品之间的内在关联,因此,本文提出一种融合图卷积神经网络的文本情感分类模型。首先,根据数据集构建了用户与用户关系图、用户与产品关系图;然后,融合两种关系图形成异质图,并使用图卷积神经网络学习用户与用户、产品与产品之间的内在联系,获得更好的用户和产品表示;最后,使用融合CNN的用户注意力和产品注意力机制的分层网络进行情感分类。实验结果表明,在公开数据集IMDB、Yelp2013和Yelp2014上,本文提出的模型能取得较好的分类效果。

关键词: 情感分类, 图卷积神经网络, 异质图, 分层网络

Abstract: Document-level sentiment classification aims to predict users sentiment polarity labels on review text. Recent studies have found that incorporating user and product information can effectively improve the performance of review sentiment classification. However, most of the pervious arts only concentrate on the relation between users and reviews, products and reviews, ignoring the inhere correlation between users and users, products and products. Therefore, we propose a sentiment classification combining graph convolution neural network. Firstly, we construct a relation graph of user-to-user and a relation graph of user-to-product based on the datasets and merge them into a heterogeneous graph. Secondly, utilize the graph convolutional neural network to learn the inhere correlation between users and users, products and products to obtain better user and product representation. Finally, use a hierarchical user attention and product attention network combined with CNN to make sentiment polarity classification. Experiments on benchmark datasets of IMDB, Yelp2013 and Yelp2014 show that our method can achieve state-of-the-art performance.

Key words: sentiment classification, graph convolutional neural network, heterogeneous graph, hierarchical network

中图分类号: 

  • TP391
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[1] 杨艳,徐冰,杨沐昀,赵晶晶. 一种基于联合深度学习模型的情感分类方法[J]. 山东大学学报（理学版）, 2017, 52(9): 19-25.
[2] 罗毅, 李利, 谭松波, 程学旗. 基于中文微博语料的情感倾向性分析[J]. 山东大学学报（理学版）, 2014, 49(11): 1-7.
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