ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2020, Vol. 57 ›› Issue (12): 2583-2595.doi: 10.7544/issn1000-1239.2020.20190854

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Text Sentiment Orientation Analysis of Multi-Channels CNN and BiGRU Based on Attention Mechanism

Cheng Yan1, Yao Leibo1, Zhang Guanghe1, Tang Tianwei2, Xiang Guoxiong3, Chen Haomai4, Feng Yue1, Cai Zhuang1   

  1. 1(School of Computer Information Engineering, Jiangxi Normal University, Nanchang 330022);2(Center of Management Decision Evaluation Research, Jiangxi Normal University, Nanchang 330022);3(School of Journalism and Communication, Jiangxi Normal University, Nanchang 330022);4(School of Mathematics and Computer, Yuzhang Normal University, Nanchang 330022)
  • Online:2020-12-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61967011), the Natural Science Foundation Project of Jiangxi Province (20202BABL202033), the Primary Research and Development Program of Jiangxi Province (20161BBE50086), the Science and Technology Key Project of Education Department of Jiangxi Province (GJJ150299), and the Humanities and Social Sciences Key (Major) Project of the Education Department (JD19056).

Abstract: CNN(convolutional neural network) and RNN(recurrent neural network) have been widely used in the field of text sentiment analysis and have achieved good results in recent years. However, there is a problem of contextual dependency between texts, although CNN can extract local features between consecutive words of a sentence, it ignores the contextual semantic information between words. BiGRU(bidirectional gated recurrent unit) network can not only solve the problem of gradient disappearance or gradient explosion in traditional RNN model, but also make up for the shortcomings that CNN can’t effectively extract contextual semantic information of long text, while it can’t extract local features as well as CNN. Therefore, this paper proposes a MC-AttCNN-AttBiGRU(multi-channels CNN and BiGRU network based on attention mechanism) model. The model can notice the important words for sentiment classification in the sentence. It combines the advantages of CNN to extract local features of text and BiGRU network to extract contextual semantic information of long text, which improves the text feature extraction ability of the model. The experimental results on the Tan Songbo Hotel Review dataset and IMDB dataset show that the proposed model can extract richer text features than other baseline models, and can achieve better classification results than other baseline models.

Key words: CNN(convolutional neural network), text sentiment orientation analysis, BiGRU(bidirectional gated recurrent unit), attention mechanism, multi-channels

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