지역사회간호학회지 (Research in Community and Public Health Nursing)

  • 제32권4호
  • /
  • Pages.467-476
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  • 2021
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  • 1225-9594(pISSN)
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  • 2288-4203(eISSN)
한국지역사회간호학회 (Korean Academy of Community Health Nursing)
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텍스트 마이닝과 토픽모델링 분석을 활용한 코로나19와 간호사에 대한 언론기사 분석

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

  • An, Jiyeon (Department of Nursing, Kyung-In Women's University) ;
  • Yi, Yunjeong (Department of Nursing, Kyung-In Women's University) ;
  • Lee, Bokim (Department of Nursing, University of Ulsan)
  • 투고 : 2021.06.11
  • 심사 : 2021.10.26
  • 발행 : 2021.12.31
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초록

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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참고문헌

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