Study on negative public opinion of Beijing public hospitals based on text mining
DU Mengkai1 WANG Lei1 DI Yang1 SHAN Yue2 YUE Xiaolin3
1.Propaganda Center, Xuanwu Hospital Capital Medical University, Beijing 100053, China;
2.Department of Party-Masses, Beijing Hospitals Authority, Beijing 100053, China;
3.Party Committee Office, Xuanwu Hospital Capital Medical University, Beijing 100053, China
Abstract：Objective To analyze the negative public opinions of municipal public hospitals in Beijing based on text mining and topic modeling. Methods All the negative public opinions of 22 municipal public hospitals in Beijing on the mainstream online media platforms from January 1 to December 31, 2021 were collected. Python 3.9 was used for text mining and corpus building. While the latent dirichlet allocation (LDA) topic model was adopted for linguistic data clustering and explaining the meaning of each cluster according to its keywords. Results A total of 3 083 pieces of linguistic data were collected, and 6 topics were extracted, forming 5 types of problems by keywords, including problems related to registration and fees, problems related to public health emergencies, problems with waiting times and service accessibility, problems in service attitude and experience of the medical process, and problems in patients’ experience of surgery and prognosis as well as hospital admissions. Conclusion Negative public opinions about public hospitals are an important source of information reflecting social conditions and public opinions. Through text mining and research on negative public opinions, management loopholes can be found effectively and improvement measures proposed, so as to enhance public opinion management in hospitals and improve medical services.
杜孟凯1 王蕾1 邸洋1 单玥2 岳小林3. 基于文本挖掘的北京市属公立医院负面舆情研究[J]. 中国医药导报, 2023, 20(9): 190-193.
DU Mengkai1 WANG Lei1 DI Yang1 SHAN Yue2 YUE Xiaolin3. Study on negative public opinion of Beijing public hospitals based on text mining. 中国医药导报, 2023, 20(9): 190-193.
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