J Korean Acad Community Health Nurs.  2021 Dec;32(4):467-476. 10.12799/jkachn.2021.32.4.467.

Analysis of Media Articles on COVID-19 and Nurses Using Text Mining and Topic Modeling

• An J
• Yi Y
• Lee B

• KMID: 2526344
• DOI: http://doi.org/10.12799/jkachn.2021.32.4.467

Abstract

Purpose
The purpose of this study is to understand the social perceptions of nurses in the context of the COVID-19 outbreak through analysis of media articles.
Methods
Among the media articles reported from January 1st to September 30th, 2020, those containing the keywords ‘[corona or Wuhan pneumonia or covid] and [nurse or nursing]’ are extracted. After the selection process, the text mining and topic modeling are performed on 454 media articles using textom version 4.5.
Results
Frequency Top 30 keywords include ‘Nurse’, ‘Corona’, ‘Isolation’, ‘Support’, ‘Shortage’, ‘Protective Clothing’, and so on. Keywords that ranked high in Term Frequency-Inverse Document Frequency (TF-IDF) values are ‘Daegu’, ‘President’, ‘Gwangju’, ‘manpower’, and so on. As a result of the topic analysis, 10 topics are derived, such as ‘Local infection’, ‘Dispatch of personnel’, ‘Message for thanks’, and ‘Delivery of one’s heart’.
Conclusion
Nurses are both the contributors and victims of COVID-19 prevention. The government and the nurses’ community should make efforts to improve poor working conditions and manpower shortages.

Keyword

COVID-19; Nurses; Data mining