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KMID : 1104420210320040467
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Research in Community and Public Health Nursing
2021 Volume.32 No. 4 p.467 ~ p.476
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Analysis of Media Articles on COVID-19 and Nurses Using Text Mining and Topic Modeling
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An Ji-Yeon
Yi Yun-Jeong
Lee Bok-Im
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Abstract
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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.
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KEYWORD
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COVID-19, Nurses, Data mining
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