Research on Sentiment-topic Recognition on Service Quality of Mixed Anxiety and Depression Disorder
[Purpose/significance]In order to identify the sentiment and topic of medical service quality in the comments of pa-tients with mixed anxiety and depression disorder in the online medical community,a service quality sentiment-topic recognition model based on CNN-BiLSTM and LDA model is proposed.[Method/process]Firstly,the CNN-BiLSTM model was constructed to extract key internal and external features of patients'comments to obtain the distribution of emotional disposition.Secondly,the LDA topic model was used to extract the topics of patients'positive and negative comments.The medical service quality topics were obtained by combining Hospital Evaluation Standards(Draft for Comments),and the positive and negative service quality was mined from the distri-bution and emotional words.[Result/conclusion]The F1 value of CNN-BiLSTM is 94.43%,which is better than other comparison models.The topics and distribution of 5-dimensional medical service quality were obtained by combining LDA topic model and related literature.The main causes of negative comments were obtained according to the topic sentiment words and their distribution,which pro-vided effective decision support for identifying and improving the quality of medical services.
online medical communityservice qualitymixed anxiety and depression disordersentiment analysistopic model
NETL
NSTL
万方数据
