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- Title
基于门控注意力的双通道情感分析及应用.
- Authors
魏龙; 胡建鹏; 张庚
- Abstract
Traditional deep learning-based text sentiment classification models usually cannot extract features completely and cannot distinguish polysemous words. To resolve these problems, a dual-channel sentiment classification model named BGA-DNet based on gated attention is proposed. The model uses the BERT pre-training model to process text data, and then extracts text features through a dual-channel network. The channel one uses TextCNN to extract local features, and the channel two uses BiLSTM-Attention to extract global features. At the same time, a gated attention unit is introduced to filter out some useless attention information. With a residual network, it also ensures that the output of the dual-channel retains the original coding information when the network reaches a saturated state. BGA-DNet is evaluated on two public datasets of hotel reviews and restaurant reviews, and compared with the latest sentiment classification methods, it achieves the best results with accuracy rate of 94.09% and 91.82%, respectively. At last, the BGA-DNet model is applied to the real dataset of students'experiment reports, and the accuracy and F1 value are also the highest.
- Subjects
DEEP learning; HOTEL ratings &; rankings; RESTAURANT reviews; HOTEL restaurants; ELECTRONIC data processing; CLASSIFICATION
- Publication
Journal of Computer Engineering & Applications, 2023, Vol 59, Issue 10, p134
- ISSN
1002-8331
- Publication type
Article
- DOI
10.3778/j.issn.1002-8331.2112-0585