Computer Science ›› 2018, Vol. 45 ›› Issue (8): 213-217,252.doi: 10.11896/j.issn.1002-137X.2018.08.038

• Artificial Intelligence • Previous Articles     Next Articles

Deeply Hierarchical Bi-directional LSTM for Sentiment Classification

ZENG Zheng1, LI Li2, CHEN Jing3   

  1. College of Journalism and Communication,Chongqing Normal University,Chongqing 401331,China1
    College of Computer and Information Science,Southwest University,Chongqing 400715,China2
    BIM Center,CSDI Engineering Co.,LTD.,Chongqing 401122,China3
  • Received:2018-05-02 Online:2018-08-29 Published:2018-08-29

Abstract: The comments on goods,films and others contribute to assess people’s preference degree for goods,which provides reference for the people who intend to buy the goods,and can help businesses adjust shelves to maximize pro-fits.In recent years,the powerful representation and learning ability in deep learning technologies provides a good support for understanding text semantics and grasping the emotional tendency of texts,especially the long short-term me-mory (LSTM) model in deep learning.The comment is a form of temporal data,which expresses semantic information through the forward arrangement of words.LSTM is a sequential model that reads the comment forward and encodes it into a real vector,and this vector implies the potential semantics of the comment and can be stored and processed by the computer.In this paper,two LSTM models are utilized to read comments from forward and backward directions respectively,and thus the two-way semantic information of the review can be obtained.Then the purpose of obtaining the deep features of comments is achieved by stacking the multilayer bidirectional LSTM.Finally,the model is put into a sentimental classification model to implement the sentiment classification.Experimental results show that the proposed method outperforms baseline LSTM,which means that deeply hierarchical bi-directional LSTM (DHBL) can capture more accurate text information.Compared with the convolutional neural network (CNN) model,the proposed model also achieves better effect.

Key words: LSTM, Deep learning, Sentiment classification

CLC Number: 

  • TP181
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