Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (11): 307-311.doi: 10.11896/jsjkx.201000075

• Artificial Intelligence • Previous Articles     Next Articles

Text Sentiment Analysis Based on Fusion of Attention Mechanism and BiGRU

YANG Qing, ZHANG Ya-wen, ZHU Li, WU Tao   

  1. Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning,Wuhan 430079,China
    School of Computer,Central China Normal University,Wuhan 430079,China
    National Language Resources Monitor & Research Center for Network Media,Wuhan 430079,China
  • Received:2020-10-13 Revised:2021-03-31 Online:2021-11-15 Published:2021-11-10
  • About author:YANG Qing,born in 1965,master,associate professor,is a member of China Computer Federation.Her main research interests include data mining and computer application technology.
    ZHANG Ya-wen,born in 1995,master.Her main research interests include data mining and computer application technology.
  • Supported by:
    National Natural Science Foundation of China(61532008) and National Key R & D Program of China(2017YFC0909502).

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Abstract: Aiming at the lack of the ability of simple neural networks to capture the contextual semantics of texts and extract important information in texts,a sentiment analysis model FFA-BiAGRU is proposed,which integrates attention mechanism and GRU.First,we pre-process the text and vectorize the words through GloVe to reduce the vector space dimension.Then,through a hybrid model that fuses the attention mechanism with the update gate of the gating unit,it can extract important information in the text features.Finally,the text features are further extracted through the forced forward attention mechanism,and then classified by the softmax classifier.Experiments on public data sets show that the algorithm can effectively improve the sentiment ana-lysis performance.

Key words: Attention mechanism, Emotion analysis, GloVe word vector, GRU

CLC Number: 

  • TP399
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