Text Sentiment Orientation Analysis Based on Multi-Channel CNN and Bidirectional GRU With Attention Mechanism

• Published in: IEEE access Vol. 8; pp. 134964 - 134975
• Main Authors: Cheng, Yan, Yao, Leibo, Xiang, Guoxiong, Zhang, Guanghe, Tang, Tianwei, Zhong, Linhui
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; attention mechanism; bidirectional gated recurrent unit network; Context modeling; Convolutional neural network; Data mining; Datasets; Feature extraction; Machine learning; Neural networks; Recurrent neural networks; Semantics; Sentiment analysis; Task analysis; text sentiment orientation analysis; Words (language)
• DOI: 10.1109/ACCESS.2020.3005823
• ISSN: 2169-3536

Convolutional Neural Network(CNN) and Recurrent Neural Network(RNN) have been widely used in the field of text sentiment analysis and have achieved good results. However, there is an anteroposterior dependency between texts, although CNN can extract local information between consecutive words of a sentence, it ignores the contextual semantic information between words. Bidirectional GRU can make up for the shortcomings that CNN can't extract contextual semantic information of long text, but it can't extract the local features of the text as well as CNN. Therefore, we propose a multi-channel model that combines the CNN and the bidirectional gated recurrent unit network with attention mechanism (MC-AttCNN-AttBiGRU). The model can pay attention to the words that are important to the sentiment polarity classification in the sentence through the attention mechanism and combine the advantages of CNN to extract local features of text and bidirectional GRU to extract contextual semantic information of long text, which improves the text feature extraction ability of the model. The experimental results on the IMDB dataset and Yelp 2015 dataset show that the proposed model can extract more rich text features than other baseline models, and can achieve better results than other baseline models.