Text Sentiment Classification Based on Deep Forests with Enhanced Features
Computer Science

Computer Science ›› 2019, Vol. 46 ›› Issue (7): 172-179.doi: 10.11896/j.issn.1002-137X.2019.07.027

• Artificial Intelligence • Previous Articles     Next Articles

Text Sentiment Classification Based on Deep Forests with Enhanced Features

HAN Hui1,WANG Li-ming1,CHAI Yu-mei1,LIU Zhen2   

  1. (School of Information Engineering,Zhengzhou University,Zhengzhou 450001,China)1
    (School of Information Science and Technology,Ningbo University,Ningbo,Zhejiang 315211,China)2
  • Received:2018-06-12 Online:2019-07-15 Published:2019-07-15

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Abstract: To effectively realize the sentiment orientation prediction of the review text,based on the deep forest model,a deep forest algorithm BFDF (Boosting Feature of Deep Forest) was proposed to classify the text.Firstly,the binary features and emotional semantic probability features are extracted.Secondly,the evaluation objects in the binary features are clustered and made features fusion.Then,the deep forest cascade characterization learning ability is improved toavoid the gradual reduction of feature information.Finally,the AdaBoost method is integrated into the deep forest,so that the deep forest notices the importance of different features,and the improved model BFDF is obtained.The experimental results on the hotel commentary corpus demonstrate the effectiveness of the proposed method.

Key words: AdaBoost, Deep forest, Feature extraction, Sentiment classification

CLC Number: 

  • TP391
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