Semantic Network Analysis of Online News and Social Media Text Related to Comprehensive Nursing Care Service.
10.4040/jkan.2017.47.6.806
- Author:
Minji KIM
1
;
Mona CHOI
;
Yoosik YOUM
Author Information
1. Center for Disaster Relief Training and Research, Severance Hospital, Seoul, Korea.
- Publication Type:Newspaper Article ; Original Article
- Keywords:
Nursing services;
Newspaper article;
Communications media;
Social media;
Semantics
- MeSH:
Boidae;
Caregivers;
Communications Media;
Humans;
Insurance, Health;
Nursing Care*;
Nursing Services;
Nursing*;
Periodicals;
Semantics*;
Social Media*
- From:Journal of Korean Academy of Nursing
2017;47(6):806-816
- CountryRepublic of Korea
- Language:Korean
-
Abstract:
PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
