摘要
目的基于网络爬虫获取上门服务平台评论数据,采用文本挖掘技术探讨用户对上门服务的关注焦点和情感态度。方法采用Python代码爬取上门护理服务平台评论数据,用狄利克雷分布主题模型挖掘获取评论潜在主题,利用ROSTCM6.0软件对评论文本数据进行高频词统计、主题情感分析等过程,挖掘护士和患者所关注的热点、核心主题,以及评论情感倾向。结果共抓取到3166条评论,主题模型识别出6个潜在主题,分别为平台功能、服务价格和效率、服务内容和专业度、护士态度及患者体验、服务对象、平台的下载和使用;评论中用户对“护士”“服务”“态度”“方便”“平台”等词提及度较高,在一定程度上展现了用户感受和关注重点;用户积极情绪占59.19%,中性情绪占27.23%,消极情绪占13.58%,用户消极情绪主要集中在服务水平存在差异、收费不合理、平台下载使用等方面。结论用户对各平台整体服务感知较好,但目前发展中仍存在服务水平参差不齐,收费不规范、平台不正规等困境,建议针对发展过程中的短板精准地实施改进,推动上门护理服务良性发展。
Objective To obtain the comment data of the on-site service platform using the web crawler,and to explore the users'focus and emotional attitude on the on-site service using text mining technology.Methods Python code was used to crawl the comment data of door-to-door nursing service platform,latent dirichlet allocation theme model mining was utilized to obtain the potential theme of comment,and ROSTCM6.0 software was used to calculate high-frequency term and analyze emotion theme,and mine the hotspots,core themes,and comment emotion tendencies of nurses and patients.Results A total of 3166 comments were captured,and the topic model identified 6 potential topics,namely platform function,service price and efficiency,service content and professionalism,nurse attitude and patient experience,service objects,and platform download and use.Words such as nurse,service,attitude,convenience and platform were highly mentioned by users,which showed the users'feelings and focus to some extent;users'positive emotions accounted for 59.19%,neutral emotions took up 27.23%,and negative emotions accounted for 13.58%,and users'negative emotions mainly focused on differences in service levels,unreasonable charges,and platform download and use,etc.Conclusion Users have a good perception of the overall service of each platform,but there are still dilemmas such as uneven service levels,irregular charges,and irregular platforms.It is recommended to improve accurately for the shortcomings in the development process,so as to promote the healthy development of door-to-door nursing services.
作者
彭佳玲
周茂林
杨青
Peng Jialing;Zhou Maolin;Yang Qing(School of Nursing,Chengdu Medical College,Chengdu 610500,China)
出处
《护理学杂志》
CSCD
北大核心
2023年第5期110-113,116,共5页
Journal of Nursing Science
基金
四川省医学会伤口疾病(泰阁)专项科研课题(2021TG05)
四川省干部保健科研课题(川干研2020-802)。
关键词
互联网+护理
护士上门服务
共享护士
网约平台
态度
文本挖掘
主题
internet plus nursing
door-to-door nursing services
nurse sharing
online appointment platform
attitude
text mining
theme
