Computer Science ›› 2022, Vol. 49 ›› Issue (5): 221-226.doi: 10.11896/jsjkx.210400135

• Artificial Intelligence • Previous Articles     Next Articles

Stance Detection Based on User Connection

LI Zi-yi, ZHOU Xia-bing, WANG Zhong-qing, ZHANG Min   

  1. School of Computer Science and Technology,Soochow University,Suzhou,Jiangsu 215006,China
  • Received:2021-04-14 Revised:2021-08-28 Online:2022-05-15 Published:2022-05-06
  • About author:LI Zi-yi,born in 1996,postgraduate,is a member of China Computer Federation.Her main research interests include na-tural language processing and so on.
    ZHOU Xia-bing,born in 1988,Ph.D,lecturer,is a member of China Compu-ter Federation.Her main research in-terests include natural language processing and so on.
  • Supported by:
    National Natural Science Foundation of China General Program(62176174).

Abstract: The main purpose of stance detection is to mine users’ attitude towards topics or events.Different from other text classification tasks,the expression about stance is more obscure,and the attitude is more sensitive to users.The current stance detection methods mainly model the information of topic content itself,which ignores the user background information.However,the information of users and their preferences greatly affects the accurate mining of text information,which enables the potential information characteristics to be obtained through the associated user information.Therefore,this paper proposes a user connection-based stance detection model (USDM),which builds a user connection by constructing a graph of users,and mines similar text stance information under the same user from a global perspective by convolution operation.At the same time,attention mechanism is added to enhance user-aware text representation.The experimental results on the public dataset H&N14 show that the proposed model achieves better performance than other models.Meanwhile,ablation experiments show that user association information and attention mechanism play an important role in improving detection accuracy.

Key words: Attention mechanism, Graph convolutional neural network, Stance detection

CLC Number: 

  • TP520
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