Lee JW, Kim Y, Han DH. LDA-based topic modeling for COVID-19-related sports research trends.
Front Psychol 2022;
13:1033872. [PMID:
36452388 PMCID:
PMC9704505 DOI:
10.3389/fpsyg.2022.1033872]
[Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 10/14/2022] [Indexed: 09/19/2023] Open
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.
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