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Kang, Kim, and Roh: A Topic Modeling Analysis for Online News Article Comments on Nurses' Workplace Bullying
Original Article

Journal of Korean Academy of Nursing 2019; 49(6): 736-747.

Published online: 30 December 2019

DOI: https://doi.org/10.4040/jkan.2019.49.6.736

A Topic Modeling Analysis for Online News Article Comments on Nurses' Workplace Bullying

Jiyeon Kang1, Soogyeong Kim2, Seungkook Roh3

1College of Nursing, Dong-A University, Busan, Korea.

2Medical Intensive Care Unit, Kosin University Gospel Hospital, Busan, Korea.

3Nuclear Policy Research Division, Korea Atomic Energy Research Institute, Daejeon, Korea.

Address reprint requests to: Kim, Soogyeong. Medical Intensive Care Unit, Kosin University Gospel Hospital, 262 Gamcheon-ro, Seo-gu, Busan 49267, Korea. Tel: +82-51-990-5273, Fax: +82-51-990-3005, happykimu2@naver.com

Received 20 March 2019       Revised 29 July 2019       Accepted 20 November 2019

© 2019 Korean Society of Nursing Science

Korean Society of Nursing Science

(open-access):

This is an Open Access article distributed under the terms of the Creative Commons Attribution NoDerivs License. (http://creativecommons.org/licenses/by-nd/4.0/) If the original work is properly cited and retained without any modification or reproduction, it can be used and re-distributed in any format and medium.

Abstract

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.

Keywords: Nurses, Workplace, Bullying, Data Mining, Sexism

Figures and Tables

Figure 1

Visualization of top 50 keywords extracted from comments on online news articles.

jkan-49-736-g001
Figure 2

Results of LDA topic modeling.

jkan-49-736-g002
Figure 3

Visualization of four topics for comments on online news articles.

jkan-49-736-g003
Table 1

Top 50 Keywords Extracted from Comments on Online News Articles

jkan-49-736-i001

TF-IDF=Term Frequency-Inverse document frequency.

Table 2

Changes in Keyword after the Major Events

jkan-49-736-i002

Notes

This work was supported by the Dong-A University research fund.

CONFLICTS OF INTEREST The authors declared no conflict of interest.

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ORCID iDs

Jiyeon Kang
https://orcid.org/0000-0002-8938-7656

Soogyeong Kim
https://orcid.org/0000-0003-1717-1036

Seungkook Roh
https://orcid.org/0000-0002-1354-335X

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