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KMID : 0806120190490060736
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대한간호학회지
2019 Volume.49 No. 6 p.736 ~ p.747
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A Topic Modeling Analysis for Online News Article Comments on Nurses’ Workplace Bullying
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Kang Ji-Yeon
Kim Soo-Gyeong
Roh Seung-Kook
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Abstract
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Purpose: This study aimed to explore public opinion on workplace bullying in the nursing field, by analyzing the keywords and topics of online news comments.
Methods: This was a text-mining study that collected, processed, and analyzed text data. A total of 89,951 comments on 650 online news articles, reported between January 1, 2013 and July 31, 2018, were collected via web crawling. The collected unstructured text data were preprocessed and keyword analysis and topic modeling were performed using R programming.
Results: The 10 most important keywords were “work” (37121.7), “hospital” (25286.0), “patients” (24600.8), “woman” (24015.6), “physician” (20840.6), “trouble” (18539.4), “time” (17896.3), “money” (16379.9), “new nurses” (14056.8), and “salary” (13084.1). The 22,572 preprocessed key words were categorized into four topics: “poor working environment”, “culture among women”, “unfair oppression”, and “society-level solutions”.
Conclusion: Public interest in workplace bullying among nurses has continued to increase. The public agreed that negative work environment and nursing shortage could cause workplace bullying. They also considered nurse bullying as a problem that should be resolved at a societal level. It is necessary to conduct further research through gender discrimination perspectives on nurse workplace bullying and the social value of nursing work.
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KEYWORD
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Nurses, Workplace, Bullying, Data mining, Sexism
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