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

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.