LDA-based topic modeling for COVID-19-related sports research trends

Jea Woog Lee; YoungBin Kim; Doug Hyun Han

doi:10.3389/fpsyg.2022.1033872

LDA-based topic modeling for COVID-19-related sports research trends

Front Psychol. 2022 Nov 14:13:1033872. doi: 10.3389/fpsyg.2022.1033872. eCollection 2022.

Authors

Jea Woog Lee¹, YoungBin Kim², Doug Hyun Han³

Affiliations

¹ Intelligent Information Processing Lab, Chung-Ang University, Seoul, South Korea.
² Department of Image Science and Arts, Chung-Ang University, Seoul, South Korea.
³ Department of Psychiatry, Chung-Ang University School of Medicine, Seoul, South Korea.

Abstract

Introduction: The COVID-19 pandemic could generate a turning point for introducing a new system for sports participation and business. The purpose of this study is to explore trends and topic structures of COVID-19-related sports research by analyzing the relevant literature.

Methods: Sports studies related to COVID-19 were collected in searching international academic databases. After the pre-processing step using the refinement and morpheme analysis function of the Net Miner program, topic modeling and social network analysis were used to analyze Journal Citation Reports found using the search term 'COVID-19 sports'.

Results: As a result, this study used subject modeling to reveal important potential topics in COVID-19-related sports research articles. 'Sports participation', 'elite players', and 'sports industry' were macroscopically classified, and detailed research topics could be identified from each division.

Conclusion: This study revealed important latent topics from COVID-19-related sports research articles using topic modeling. The results of the research elucidate the structure of academic knowledge on this topic and provide guidance for future research.

Keywords: COVID-19; LDA algorithm; data science; research trend; sport; topic modeling.