JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2021, Vol. 56 ›› Issue (11): 15-23.doi: 10.6040/j.issn.1671-9352.0.2021.425

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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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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

CLC Number: 

  • TP391
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