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http://dx.doi.org/10.9708/jksci.2020.25.12.083

Analysis of Research Topics and Trends on COVID-19 in Korea Using Latent Dirichlet Allocation (LDA)  

Heo, Seong-Min (Dept. of Applied Mathematics, Kumoh National Institute of Technology)
Yang, Ji-Yeon (Dept. of Applied Mathematics, Kumoh National Institute of Technology)
Publication Information
Journal of the Korea Society of Computer and Information / v.25, no.12, 2020 , pp. 83-91 More about this Journal
Abstract
This study aims to identify research topics and examine the trend of Covid19-related papers on DBpia. Applying latent Dirichlet allocation (LDA), we have extracted seven research topics, each of which concerns "International Dynamics", "Technology & Security", "Psychological Impact", "Biomedical-Related", "Economic Impact", "Online Education", and "Religion-Related". In addition, we used the multinomial logistic model to examine the trend of research topics. We found that the papers mainly cover topics related to "International Dynamics" and "Biomedical-Related" before June 2020, but the topics have become diverse since then. In particular, topics regarding "Economic Impact", "Online Education" and "Psychological Impact" has drawn increased attention of researchers. The findings would provide a guideline for collaboration in Covid19-related research, and could serve as a reference work for active research.
Keywords
Covid19; text mining; latent Dirichlet allocation; research topic; multinomial logistic model;
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