Cross-Domain Text Sentiment Classification Method Based on the CNN-BiLSTM-TE Model


Yuyang Zeng, Ruirui Zhang, Liang Yang, Sujuan Song, Journal of Information Processing Systems Vol. 17, No. 4, pp. 818-833, Aug. 2021  

10.3745/JIPS.04.0221
Keywords: "Bidirectional Long Short-Term Memory, Convolutional Neural Network, Deep Learning, sentiment analysis, Topic Extraction"
Fulltext:

Abstract

To address the problems of low precision rate, insufficient feature extraction, and poor contextual ability in existing text sentiment analysis methods, a mixed model account of a CNN-BiLSTM-TE (convolutional neural network, bidirectional long short-term memory, and topic extraction) model was proposed. First, Chinese text data was converted into vectors through the method of transfer learning by Word2Vec. Second, local features were extracted by the CNN model. Then, contextual information was extracted by the BiLSTM neural network and the emotional tendency was obtained using softmax. Finally, topics were extracted by the term frequencyinverse document frequency and K-means. Compared with the CNN, BiLSTM, and gate recurrent unit (GRU) models, the CNN-BiLSTM-TE model’s F1-score was higher than other models by 0.0147, 0.006, and 0.0052, respectively. Then compared with CNN-LSTM, LSTM-CNN, and BiLSTM-CNN models, the F1-score was higher by 0.0071, 0.0038, and 0.0049, respectively. Experimental results showed that the CNN-BiLSTM-TE model can effectively improve various indicators in application. Lastly, performed scalability verification through a takeaway dataset, which has great value in practical applications.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from November 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.




Cite this article
[APA Style]
Yuyang Zeng, Ruirui Zhang, Liang Yang, & Sujuan Song (2021). Cross-Domain Text Sentiment Classification Method Based on the CNN-BiLSTM-TE Model. Journal of Information Processing Systems, 17(4), 818-833. DOI: 10.3745/JIPS.04.0221.

[IEEE Style]
Y. Zeng, R. Zhang, L. Yang and S. Song, "Cross-Domain Text Sentiment Classification Method Based on the CNN-BiLSTM-TE Model," Journal of Information Processing Systems, vol. 17, no. 4, pp. 818-833, 2021. DOI: 10.3745/JIPS.04.0221.

[ACM Style]
Yuyang Zeng, Ruirui Zhang, Liang Yang, and Sujuan Song. 2021. Cross-Domain Text Sentiment Classification Method Based on the CNN-BiLSTM-TE Model. Journal of Information Processing Systems, 17, 4, (2021), 818-833. DOI: 10.3745/JIPS.04.0221.