LDA를 이용한 코로나 19 국내 연구토픽 및 동향 분석
Analysis of Research Topics and Trends on COVID-19 in Korea Using Latent Dirichlet Allocation (LDA)
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초록
본 연구에서는 DBpia에 등록된 코로나19 관련 논문을 대상으로 연구 토픽을 밝히고 연구 변화추세를 검토한다. 잠재 디리슐레 할당(Latent Dirichlet Allocation) 알고리즘을 적용한 결과, 7개의연구 토픽을 도출하였고, 각 토픽은 "International Dynamics", "Technology & Security", "Psychological Impact", "Biomedical-Related", "Economic Impact", "Online Education", "Religion-Related" 에 관한 내용이었다. 또한 다범주 로짓모형을 사용하여 연구 토픽의 추세 변화를 살펴본 결과, 2020년 6월 전에는 국제적 역학관계 및 생물 의학 관련 논문이 주를 이루었다면, 이후에는 다양한 분야로 연구 주제가 확대되었다. 특히 경제적인 영향, 온라인 교육, 심리적인 영향에 관한 연구가 꾸준히 증가함을 확인할 수 있었다. 이러한 결과는 향후 코로나19 관련 공동 연구의 가이드라인을 제시하고, 활발한 연구 활동을 위한 기초자료로 활용될 수 있을 것이다.
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.
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