Sentiment Analysis of Online Medical Reviews Based on BERT and Semantics Collaboration through Dual-channel

Corresponding author: ZHANG Jian-tong, zhangjiantong@tongji.edu.cn

DOI: 10.12201/bmr.202407.00042

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  Abstract: Purpose/significance With the rapid development of online healthcare platforms, numerous patient feedback has been amassed. Leveraging artificial intelligence to ac-curately discern the sentiment polarity from the vast corpus of comments and swiftly identifying negative feedback holds significant practical implications for hospital op-erations and physician managements. Method/process Taking comments from Haodf.com as an example, this paper first uses BERT to generate word embeddings, which are then fed into a convolutional layer and a BiLSTM network in a du-al-channel manner. Finally, a feature fusion strategy is employed to obtain textual sen-timent information to achieve a binary classification task. Result/conclusion The ex-perimental results demonstrate that the proposed dual-channel model based on BERT can better integrate the advantages of CNN and BiLSTM. It achieves the highest clas-sification accuracy and macro F1-score compared to other 9 models, including BERT, BERT_CNN and BERT_BiLSTM, which highlights the effectiveness of the proposed model in sentiment classification tasks for online medical review.

  Key words: BERT; CNN; BiLSTM; online medical reviews; sentiment classification; dual-channel

  Submit time: 17 July 2024

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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