Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (1): 186-192.doi: 10.11896/jsjkx.181002011

• Artificial Intelligence • Previous Articles     Next Articles

Comment Sentiment Analysis and Sentiment Words Detection Based on Attention Mechanism

LI Yuan,LI Zhi-xing,TENG Lei,WANG Hua-ming,WANG Guo-yin   

  1. (Chongqing Key Lab of Computation Intelligence,Chongqing 400065,China)
  • Received:2018-10-31 Published:2020-01-19
  • About author:LI Yuan,born in 1992,master.Her main research interests include deep learning and network security;WANG Guo-yin,born in 1970,Ph.D,professor,Ph.D supervisor.His main research interests include rough set,granular computing,data mining and machine learning.
  • Supported by:
    This work was supported by the National Key R&D Program of China (2017YFB0802300),National Natural Science Foundation of China (61502066) and Chongqing Basic and Frontier Research Project (cstc2015jcyjA40018).

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Abstract: Comment sentiment analysis is one of the research hotspots in user generated content field.Because of the diversity of comment objects and the casualness of commentators’ language,comment sentiment analysis has become a challenging issue.The existing methods mainly calculate the emotional polarity of comments by pre-building the emotional vocabulary.However,these methods cannot adapt to the problem that the same words have different emotional polarities in different contexts.To overcome this problem,the attention based convolutional-recurrent neural network (A-CRNN) model was proposed to model the emotional polarity of comments and words in different contexts.By combining the context of words in sentences,the proposed method can focus attention on a small scale around the main emotional words.The A-CRNN model calculates the emotional polarity of the words through an adaptive method,which improves the accuracy of words’ emotional polarity judgment and the accuracy of short texts’ emotional polarity.Compared with CRNN,CNN and emotional dictionary methods,the proposed method achieves better results in Chinese dataset induding Meituan Review,Party Building Review and English dataset including Amazon Product Review.

Key words: Attention mechanism, Convolutional-recurrent neural network, Emotional analysis, Multi-granularity

CLC Number: 

  • TP391
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