Computer Science ›› 2021, Vol. 48 ›› Issue (8): 226-233.doi: 10.11896/jsjkx.200700058

Special Issue: Natural Language Processing

• Artificial Intelligence • Previous Articles     Next Articles

Fine-grained Sentiment Analysis Based on Combination of Attention and Gated Mechanism

ZHANG Jin, DUAN Li-guo, LI Ai-ping, HAO Xiao-yan   

  1. College of Information and Computer,Taiyuan University of Technology,Taiyuan 030024,China
  • Received:2020-07-09 Revised:2020-08-14 Published:2021-08-10
  • About author:ZHANG Jin,born in 1996,postgra-duate.Her main research interests include sentiment analysis and natural language processing.( Li-guo,born in 1970,Ph.D,associate professor,is a senior member of China Computer Federation.His main research interests include automatic question answering system,text sentiment analysis,entity relationship extraction and knowledge mapping.
  • Supported by:
    Basic Research Project of Shanxi Province(201801D121137).

Abstract: The fine-grained sentiment analysis is one of the key problems in the area of natural language processing.By learning contextual information of the text to conduct sentiment analysis on specific aspects,it can help users and businesses to better understand the sentiment information of specific aspects of users' comments.Aiming at the task of fine-grained sentiment analysis on users' comments,a text sentiment classification model combining BiGRU-attention and Gated Mechanisms is proposed.By integrating existing sentiment resources,HOWNET evaluation sentiment dictionary is used as the seed sentiment dictionary to expand the user comment sentiment dictionary through SO-PMI algorithm,the negative dictionary and part of speech information are combined to expand the user comment sentiment knowledge as the users' comment sentiment characteristic information.Introducing word,character and sentiment characteristics as the model of input infotmation,and using BiGRU to extract deep text features,then combined with gated mechanism as well as the attention mechanism,according to the acquired aspect word information to further extract the contextual sentiment characteristics related to aspect words,the final sentiment polarity is obtained by the softmax classfier.Experimental results show that the proposed model achieves better experimental results on the AIchallenger 2018 fine-grained sentiment analysis Chinese data sets,the Macro_F1_ score value reaches 0.7218,and the performance exceeds the baseline system.

Key words: Attention mechanism, BiGRU, Deep learning, Gated mechanism, Sentiment analysis

CLC Number: 

  • TP391
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