JIPS (한국정보처리학회)
Current Result Document : 2 / 2
| 한글제목(Korean Title) |
Cross-Domain Text Sentiment Classification Method Based on the CNN-BiLSTM-TE Model |
| 영문제목(English Title) |
Cross-Domain Text Sentiment Classification Method Based on the CNN-BiLSTM-TE Model |
| 저자(Author) |
Yuyang Zeng
Ruirui Zhang
Liang Yang
Sujuan Song
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| 원문수록처(Citation) |
VOL 17 NO. 04 PP. 0818 ~ 0833 (2021. 08) |
한글내용
(Korean Abstract) |
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영문내용
(English 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. |
| 키워드(Keyword) |
Bidirectional Long Short-Term Memory
Convolutional Neural Network
Deep Learning
Sentiment Analysis
Topic Extraction
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| 파일첨부 |
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