Paper
6 May 2022 Sentiment analysis of online reviews based on BERT-BiLSTM-CBAM
Juan Chen, Ruyun Chen, Jialu Zhao
Author Affiliations +
Proceedings Volume 12176, International Conference on Algorithms, Microchips and Network Applications; 121762E (2022) https://doi.org/10.1117/12.2636392
Event: International Conference on Algorithms, Microchips, and Network Applications 2022, 2022, Zhuhai, China
Abstract
In this paper, a hybrid model based on BERT-BiLSTM-CBAM is proposed to classify the sentiment of online reviews more accurately. Firstly, the BERT model is used to pre-train the text information to obtain feature vectors; then the feature vectors obtained from Bert pre-training are stitched and reorganized through a bi-directional long and short-term memory network (BiLSTM) and CBAM mechanism to obtain new feature vectors. Finally, these new feature vectors are input to the fully connected layer, and the sentiment category of the text is calculated by the SoftMax function. Experiments on the Amazon reviews and Yelp reviews datasets show that the method is more accurate and reliable on both datasets.
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Juan Chen, Ruyun Chen, and Jialu Zhao "Sentiment analysis of online reviews based on BERT-BiLSTM-CBAM", Proc. SPIE 12176, International Conference on Algorithms, Microchips and Network Applications, 121762E (6 May 2022); https://doi.org/10.1117/12.2636392
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KEYWORDS
Data modeling

Computer programming

Internet

Analytical research

Transformers

Associative arrays

Machine learning

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