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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