|
|
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.
|
|
|
|
|
[1] CNNIC发布第49次《中国互联网络发展状况统计报告》[EB/OL].中国互联网络信息中心,2022(2022-02- 25.).http://cnnic.cn/gywm/xwzx/rdxw/20172017_7086/2022 02/t20220225_71725.htm.
[2] 吕朝辉,程子恒.重大疫情防控中的网络舆情及其信息治理策略——基于“弹簧”动力模型分析[J].情报杂志,2021,40(1):150-156,164.
[3] 杜孟凯,王蕾,张国君,等.以敏感舆情为抓手提升医院管理服务水平的思考[J].中国医院,2019,23(6):69-71.
[4] 赵波.新媒体时代三级公立医院网络舆情管理策略研究——基于八例医疗舆情案例[J].中华临床医师杂志(电子版),2020,14(10):840-842.
[5] 北京市属公立医院名单[EB/OL].北京市医院管理中心,2019(2019-12-04).http://www.bjygzx.org.cn/jgzn/ssyy/index.htm.
[6] 韩栋,王春华,肖敏.结合半监督学习和LDA模型的文本分类方法[J].计算机工程与设计,2018,39(10):3265-3271.
[7] 孙宗缘,马秀峰,李奇.突发公共安全事件网络舆情演化分析——基于文本挖掘与情感分析两个视角[J].河北科技图苑,2021,34(5):65-75.
[8] 张瑶,夏晨曦,马敬东.某医院患者投诉信息中服务体验主题建模与情感分析[J].中华医院管理杂志,2019,35(12):1037-1041.
[9] 吴江,侯绍新,靳萌萌,等.基于LDA模型特征选择的在线医疗社区文本分类及用户聚类研究[J].情报学报,2017, 36(11):1183-1191.
[10] 龚海燕.新媒体环境下医院危机管理中的舆情应对[J].中国卫生标准管理,2019,10(18):14-16.
[11] 杨红.应急管理视角下做好网络舆情应对工作的四个抓手——基于北京市应急管理舆情工作实践的思考[J].科技传播,2022,14(10):90-92,102.
[12] 王璐,董琳,陈明雁,等.融媒体时代医院网络舆情管理模式探索[J].中国医院,2021,25(5):87-88.
[13] 王蕾,杜孟凯,张国君,张维.医院宣传绩效管理的实践与思考[J].中国医药导报,2019,16(31):160-163,172.
[14] 姚峥,王蕾,李小宇,等.基于建立网络舆情反馈和处置绿色通道的改善门诊医疗服务实践[J].中国医院,2019, 23(8):16-18.
[15] 严建军,高新跃,姚海宏,等.区域性医疗中心宣传舆情系统应对突发公共卫生事件的策略探讨[J].中国医药导报,2020,17(20):190-193.
[16] 董向慧.舆情视角下的突发公共卫生事件风险沟通框架建构[J].理论与改革,2020(4):14-23.
[17] 刘光牛,毛伟.科学精准把握时度效 全方位提升舆论引导能力[J].中国记者,2022(1):52-57.
[18] 宋良壁.浅议“议程设置”在电视宣传报道中的运用[J].新闻研究导刊,2019,10(16):149-150.
[19] 闻庆柱,尹文强,黄亚男,等.山东省五所三级综合医院医疗投诉分析[J].中华医院管理杂志,2018,34(7):600-603.
[20] 邹军.中国网络舆情综合治理体系的构建与运作[J].南京师大学报(社会科学版),2020(2):116-126.
[21] 钟小婷.从医患纠纷视角剖析医院管理制度建设[J].中国卫生标准管理,2020,11(19):31-33.
[22] 项春梅,丁枭伟,吴峥,等.社会主义核心价值观社会层面的培育对公立医院医德医风的影响[J].中国医院,2020,24(11):22-25.
[23] 王淑新.新时期医德医风建设在临床护理中的应用[J].中国当代医药,2020,27(31):209-211,215.
[24] 章洁.把典型人物放在共情传播视角下[J].传媒评论,2020(1):31-33.
[25] 胡诚,张维,王蕾,等.新冠肺炎疫情防控期间医院舆论引导和宣传教育工作的实践和体会[J].中国医院,2020, 24(10):68-70.
[26] 杨彩虹.积极心理学在医院思想政治工作中的应用及思考[J].中国卫生产业,2018,15(31):185-187. |
|
|
|