Computer Science ›› 2020, Vol. 47 ›› Issue (10): 222-227.doi: 10.11896/jsjkx.190900173

• Artificial Intelligence • Previous Articles     Next Articles

Comment Sentiment Classification Using Cross-attention Mechanism and News Content

WANG Qi-fa, WANG Zhong-qing, LI Shou-shan, ZHOU Guo-dong   

  1. School of Computer Science and Technology,Soochow University,Suzhou,Jiangsu 215006,China
  • Received:2019-09-25 Revised:2019-12-06 Online:2020-10-15 Published:2020-10-16
  • About author:WANG Qi-fa,born in 1994,postgra-duate,is a member of China Computer Federation.His main research interests include natural language processing and emotion analysis.
    WANG Zhong-qing,born in 1987,Ph.D,postgraduate supervisor,is a member of China Computer Federation.His main research interests include natural language understanding and information extraction.
  • Supported by:
    Young Scientists Fund of the National Natural Science Foundation of China (61806137,61702518) and Natural Science Foundation of the Jiangsu Higher Education Institutions of China (18KJB520043)

Abstract: At present,news comment has become important news derived data.News comment expresses commentators’ views,positions and personal feelings on news events.Through the analysis of sentiment orientation of news comment,it is helpful to understand the social public opinion and trend.Therefore,the sentiment research of news comment is favored by many scholars.The usual news comment sentiment analysis only considers the information of the comment text itself.However,news comment text information is often closely related to news content information.Based on this,this paper proposed a comment sentiment classification method using cross-attention mechanism and combined with news content.Firstly,the bi-directional long short-term memory network model is used to characterize the news content and the comment text respectively.Then,the cross-attention mechanism is used to further capture important information,and obtain the vector representation of the two updated news content texts and comment texts.And then the semantic representation obtained by splicing them together is input into the full connection layer,and sigmoid activation function is used for classification prediction,so as to realize the sentiment classification of news comments.The results show that the model of comment sentiment classification using cross-attention mechanism and news content can effectively improve the accuracy of sentiment classification of news comment,and this model improves by 1.72%,3.24% and 6.21% on F1 respectively compare with the three benchmark models.

Key words: Attention mechanism, Bi-directional Long Short-Term Memory Network, Sentiment Classification

CLC Number: 

  • TP391.1
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