Kim Minji, Choi Mona, Youm Yoosik
Center for Disaster Relief Training and Research, Severance Hospital, Seoul, Korea.
College of Nursing · Mo-Im Kim Nursing Research Institute, Yonsei University, Seoul, Korea.
J Korean Acad Nurs. 2017 Dec;47(6):806-816. doi: 10.4040/jkan.2017.47.6.806.
As comprehensive nursing care service has gradually expanded, it has become necessary to explore the various opinions about it. The purpose of this study is to explore the large amount of text data regarding comprehensive nursing care service extracted from online news and social media by applying a semantic network analysis.
The web pages of the Korean Nurses Association (KNA) News, major daily newspapers, and Twitter were crawled by searching the keyword 'comprehensive nursing care service' using Python. A morphological analysis was performed using KoNLPy. Nodes on a 'comprehensive nursing care service' cluster were selected, and frequency, edge weight, and degree centrality were calculated and visualized with Gephi for the semantic network.
A total of 536 news pages and 464 tweets were analyzed. In the KNA News and major daily newspapers, 'nursing workforce' and 'nursing service' were highly rated in frequency, edge weight, and degree centrality. On Twitter, the most frequent nodes were 'National Health Insurance Service' and 'comprehensive nursing care service hospital.' The nodes with the highest edge weight were 'national health insurance,' 'wards without caregiver presence,' and 'caregiving costs.' 'National Health Insurance Service' was highest in degree centrality.
This study provides an example of how to use atypical big data for a nursing issue through semantic network analysis to explore diverse perspectives surrounding the nursing community through various media sources. Applying semantic network analysis to online big data to gather information regarding various nursing issues would help to explore opinions for formulating and implementing nursing policies.
随着综合护理服务的逐渐扩展，有必要探索关于它的各种观点。本研究的目的是通过应用语义网络分析来探索从在线新闻和社交媒体中提取的有关综合护理服务的大量文本数据。
使用Python搜索关键词“综合护理服务”，抓取韩国护士协会（KNA）新闻、主要日报和推特的网页。使用KoNLPy进行形态分析。选择“综合护理服务”集群上的节点，并计算频率、边权重和度中心性，并用Gephi对语义网络进行可视化。
共分析了536篇新闻页面和464条推文。在KNA新闻和主要日报中，“护理人员”和“护理服务”在频率、边权重和度中心性方面得分较高。在推特上，最频繁出现的节点是“国民健康保险服务”和“综合护理服务医院”。边权重最高的节点是“国民健康保险”、“无护理人员病房”和“护理费用”。“国民健康保险服务”的度中心性最高。
本研究提供了一个如何通过语义网络分析将非典型大数据用于护理问题的示例，以通过各种媒体来源探索围绕护理界的不同观点。将语义网络分析应用于在线大数据以收集有关各种护理问题的信息，将有助于探索制定和实施护理政策的意见。
