Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (11A): 221000217-7.doi: 10.11896/jsjkx.221000217

• Artificial Intelligence • Previous Articles     Next Articles

Multi-feature Fusion Based New Personalized Sentiment Classification Method for Comment Texts

WANG Youwei1, LIU Ao1, FENG Lizhou2   

  1. 1 School of Information,Central University of Finance and Economics,Beijing 100081,China
    2 School of Science and Engineering,Tianjin University of Finance and Economics,Tianjin 300222,China
  • Published:2023-11-09
  • About author:WANG Youwei,born in 1987,Ph.D,associate professor,is a member of China Computer Federation.His main researchinterests include machine learning,data mining and NLP.
    LIU Ao,born in 1997,postgraduate.His main research interests include data mining and NLP.
  • Supported by:
    National Natural Science Foundation of China(61906220),Ministry of Education of Humanities and Social Science Project(19YJCZH178),National Social Science Foundation of China(18CTJ008) and Emerging Interdisciplinary Project of CUFE.

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Abstract: Existing research on sentiment classification fails to fully consider the influence of personality characteristics contained in user’s personal historical comments on the results of sentiment classification,and fails to comprehensively consider the combined effects of many factors such as user’s social relations,personal attributes,historical comments and current comments.To this end,a new personalized method for sentiment classification of comment texts based on multi-feature fusion is proposed.First,the user’s personality expressions is mined by using a great number of unlabeled user’s historical comments,and the user’s feature vector is extracted by combining user’s historical comments and attribute information.Then,the advantages of the node2vec algorithm in obtaining the node representation of the graph are used to learn users’ social relationship networks,so as to obtain the users’ social relationship vectors,and the pre-trained word2vec model is used to obtain the user’s current comment vector.Finally,the user’s feature vector,social relationship vector and labeled current comment vector are entered into the fully connected classifier for training to obtain the final classification model.Experimental results on the real data set crawled from the Chinese stock page show that compared with typical methods such as support vector machine,naive Bayes,TextCNN,Bert,the proposed method can effectively improve the accuracy and F1 value of sentiment classification,which verifies its effectiveness in improving sentiment classification performance.

Key words: Sentiment classification, Stock comments, Social relations, Historical comments, Full connect neural network

CLC Number: 

  • TP391
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