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LDA-based topic modeling for COVID-19-related sports research trendsopen access

Authors
Lee, J.W.Kim, Y.Han, D.H.
Issue Date
Nov-2022
Publisher
Frontiers Media S.A.
Keywords
COVID-19; data science; LDA algorithm; research trend; sport; topic modeling
Citation
Frontiers in Psychology, v.13
Journal Title
Frontiers in Psychology
Volume
13
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/71548
DOI
10.3389/fpsyg.2022.1033872
ISSN
1664-1078
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. Copyright © 2022 Lee, Kim and Han.
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