Sentiment Analysis of Online Medical Reviews Based on BERT and Semantics Collaboration through Dual-channel
10.3969/j.issn.1673-6036.2024.11.005
- VernacularTitle:基于BERT和双通道语义协同的在线医疗评论情感分析
- Author:
Wen ZHANG
1
;
Jiantong ZHANG
;
Yushan GUO
Author Information
1. 同济大学经济与管理学院 上海 200092
- Keywords:
bidirectional encoder representations from transformers(BERT);
convolutional neural network(CNN);
bidirectional long short-term memory(BiLSTM);
online medical reviews;
sentiment classification;
dual-channel model
- From:
Journal of Medical Informatics
2024;45(11):30-35
- CountryChina
- Language:Chinese
-
Abstract:
Purpose/Significance To use artificial intelligence(AI)technology to quickly screen negative comments from a large num-ber of reviews,so as to understand the needs and grievances of patients,and promote the sustainable development of telemedicine.Meth-od/Process Taking comments from Haodf.com as an example,the paper first uses bidirectional encoder representations from transformers(BERT)to generate word embeddings,which are then fed into a convolutional neural network(CNN)and a bidirectional long short-term memory(BiLSTM)network in a dual-channel manner.Finally,a feature fusion strategy is employed to obtain textual sentiment informa-tion to achieve a binary classification task.Result/Conclusion The proposed dual-channel model based on BERT can better integrate the advantages of CNN and BiLSTM.It achieves the highest classification accuracy and macro F1-score compared to other 9 models,including BERT,BERT_BiLSTM,BERT_CNN,etc.,which is effective in sentiment classification tasks for online medical reviews.
