Computer Science ›› 2018, Vol. 45 ›› Issue (8): 191-197.doi: 10.11896/j.issn.1002-137X.2018.08.034

• Artificial Intelligence • Previous Articles     Next Articles

Emotion Classification for Readers Based on Multi-view Multi-label Learning

WEN Wen1, CHEN Ying1, CAI Rui-chu1, HAO Zhi-feng1,2, WANG Li-juan1   

  1. Department of Computers,Guangdong University of Technology,Guangzhou 510000,China1
    Department of Mathematics and Big Data,Foshan University,Foshan,Guangdong 528000,China2
  • Received:2017-07-26 Online:2018-08-29 Published:2018-08-29

Abstract: The traditional emotion classification for readers mainly focuses on the emotional polarity embodied in the reader’s comments,which is from the perspective of sentiment analysis.However,the readers’ comments are occasio-nally not collected due to some reasons,which tends to reduce the effectiveness and timeliness of emotional classification.How to integrate the multi-perspective information,including news texts and readers’ comments,and to make a more accurate judgment of reader’s emotions has become a challenging problem.In this paper,a multi-view multi-label latent indexing (MV-MLSI) model was proposed,which maps the multi-view text features from different perspectives to the low-dimensional semantic space.Meanwhile,the mapping function among the features and labels was established,and the model could be solved by minimizing the reconstruction error.The optimization algorithm was also presented in this paper so as to make the effective prediction of reader’s emotion.Compared with the traditional model,the proposed model can not only take full advantage of multi-view information,but also take into account the correlation among labels.Experiments on the multi-view news text dataset demonstrate that the method can achieve higher accuracy and stability.

Key words: Emotion classification, Sentiment analysis, Multi-label learning, Multi-view learning, Latent semantic indexing

CLC Number: 

  • TP391
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