Computer Science ›› 2022, Vol. 49 ›› Issue (1): 252-258.doi: 10.11896/jsjkx.210600063

Special Issue: Natural Language Processing

• Artificial Intelligence • Previous Articles     Next Articles

Self-attention-based BGRU and CNN for Sentiment Analysis

HU Yan-li, TONG Tan-qian, ZHANG Xiao-yu, PENG Juan   

  1. Science and Technology on Information Systems Engineering Laboratory,National University of Defense Technology,Changsha 410073,China
  • Received:2021-06-04 Revised:2021-09-10 Online:2022-01-15 Published:2022-01-18
  • About author:HU Yan-li,born in 1979,associate professor,is a member of China Computer Federation.Her main research interests include text mining and knowledge engineering.
  • Supported by:
    National Natural Science Foundation of China(61302144,61902417).

Abstract: Text sentiment analysis is a hot field in natural language processing.In recent years,Chinese text sentiment analysis methods have been widely investigated.Most of the recurrent neural network and convolutional neural network models based on word vectors have insufficient ability to extract and retain text features.In this paper,a Chinese sentiment polarity analysis model combining bi-directional GRU (BGRU) and multi-scale CNN is proposed.First,BGRU is utilized to extract text serialization features filtered with attention mechanism.Then the convolution neural network with distinct convolution kernels is applied to attention mechanism to adjust the dynamic weights.The text is acquired by the Softmax emotional polarity.Experiments indicates that our model outperforms the state-of-the-art methods on Chinese datasets.The accuracy of sentiment classification is 92.94% on the online_shopping_10_cats dataset of ChineseNLPcorpus,and 92.75% on the hotel review dataset compiled by Tan Songbo of Chinese Academy of Sciences,which is significantly improved compared with the current mainstream methods.

Key words: Bi-directional gated recurrent unit, Multi-scale convolution neural network, Self-attention mechanism, Sentiment analysis

CLC Number: 

  • TP391
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