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KMID : 1812920230050020079
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Korean Journal of Occupational Health
2023 Volume.5 No. 2 p.79 ~ p.90
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Research Trends and Article Analysis Related to Overwork Using Text Mining
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Baek Eun-Mi
Lee Hyun-Ju
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Abstract
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Purpose : The purpose of this study is to provide basic data to ultimately prevent overwork in the workplace by extracting words about overwork in the workplace from media reports and articles and analysing keywords that form a semantic network.
Methods : Data collection, data pre-processing and data analysis were carried out to analyse perceptions of overwork in the workplace.
Results : The study analysed a total of 74,147 news articles and keywords from 590 national newspapers between January 2018 and February 2023 to identify trends in 'workplace overwork'. A time-series analysis of the news articles identified the main issues that occurred in the data from over 70,000 articles and presented them in a time-series chart by month to identify the issues during the period when the number of data increased significantly. However, the time series analysis of the national articles showed that about 110 articles related to 'overwork in the workplace' were published each year, but were judged not to have led to research activities, despite the occurrence of various social problems.
Conclusion : The results of this study show that 'work overload' can occur in all occupations and is particularly prevalent among those who work in management and for their own livelihood, so its health effects also need to be managed.
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KEYWORD
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Overwork, Text mining, Research trends, Article analysis
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