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

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2020, 25(12), pp.83-91
  • DOI : 10.9708/jksci.2020.25.12.083
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : November 18, 2020
  • Accepted : December 7, 2020
  • Published : December 31, 2020

Seong-Min Heo 1 yang ji yeon 1

1금오공과대학교

Accredited

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.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.