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텍스트 마이닝과 토픽모델링 분석을 활용한 코로나19와 간호사에 대한 언론기사 분석

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Alternative Title
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.
Author(s)
안지연이복임이윤정
Issued Date
2021
Type
Article
Keyword
COVID-19NursesData mining
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9448
Publisher
지역사회간호학회지(J Korean Acad Community Health Nurs)
Location
대한민국
Language
한국어
ISSN
1225-9594
Citation Volume
32
Citation Number
4
Citation Start Page
467
Citation End Page
476
Appears in Collections:
Medicine > Nursing
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