International Journal of Internet, Broadcasting and Communication

  • 제15권3호
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  • Pages.211-218
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  • 2023
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  • 2288-4920(pISSN)
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  • 2288-4939(eISSN)
한국인터넷방송통신학회 (The Institute of Internet, Broadcasting and Communication)
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Big Data Analysis on the Perception of Home Training According to the Implementation of COVID-19 Social Distancing

  • Hyun-Chang Keum (Department of Sport Interaction Science, The Graduate School of Sungkyunkwan University) ;
  • Kyung-Won Byun (Department of graduate school of business Administration, Dankook University)
  • 투고 : 2023.07.19
  • 심사 : 2023.07.27
  • 발행 : 2023.08.31
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초록

Due to the implementation of COVID-19 distancing, interest and users in 'home training' are rapidly increasing. Therefore, the purpose of this study is to identify the perception of 'home training' through big data analysis on social media channels and provide basic data to related business sector. Social media channels collected big data from various news and social content provided on Naver and Google sites. Data for three years from March 22, 2020 were collected based on the time when COVID-19 distancing was implemented in Korea. The collected data included 4,000 Naver blogs, 2,673 news, 4,000 cafes, 3,989 knowledge IN, and 953 Google channel news. These data analyzed TF and TF-IDF through text mining, and through this, semantic network analysis was conducted on 70 keywords, big data analysis programs such as Textom and Ucinet were used for social big data analysis, and NetDraw was used for visualization. As a result of text mining analysis, 'home training' was found the most frequently in relation to TF with 4,045 times. The next order is 'exercise', 'Homt', 'house', 'apparatus', 'recommendation', and 'diet'. Regarding TF-IDF, the main keywords are 'exercise', 'apparatus', 'home', 'house', 'diet', 'recommendation', and 'mat'. Based on these results, 70 keywords with high frequency were extracted, and then semantic indicators and centrality analysis were conducted. Finally, through CONCOR analysis, it was clustered into 'purchase cluster', 'equipment cluster', 'diet cluster', and 'execute method cluster'. For the results of these four clusters, basic data on the 'home training' business sector were presented based on consumers' main perception of 'home training' and analysis of the meaning network.

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참고문헌

  1. M. G. Kim, "Social Changes and Social Policy Paradigm in Times of Covid-19," Health and welfare policy forum, Vol. 290, pp. 6-19, 2020.
  2. I. J. Hwang, and H. J. Cheon, "People who Exercise Alone at Home: Characteristics by Types and their Continuous Intention," Journal of Leisure Studies, Vol. 20, No. 3, pp. 55-79, 2022. DOI: https://doi.org/10.22879/slos.2022.20.3.55
  3. N. K. Lee, "A Structural Analysis on the Effects of Consumer Value, Satisfaction, and Purchase Intention on Home Training Consumption of Home Training Consumers," (Unpublished doctoral dissertation). Sungshin Women's University, Seoul, Korea, 2022.
  4. H. M. Jeon, and Y. H. Pan, "A Classification of Type and User Interaction Model of Online Home Training Service in the Social Distancing Environment: Based on the Service Design Perspective," Design Convergence Study, Vol. 19, No. 4, pp. 15-30, 2020. DOI: https://doi.org/10.31678/SDC83.2
  5. M. C. Lee, J. S. Lee, D. I. Han, and K. H. Han, "Changes in the Perception of Home Training Using Text Mining Techniques: Comparison between before and after the COVID-19 Pandemic," Journal of Health Informatics and Statistics, Vol. 48, No. 1, pp. 15-25, 2023. DOI: https://doi.org/10.21032/ jhis.2023.48.1.15
  6. Korea Consumer Agency(2019, February). Home Training Hazard Analysis. Safety Report. pp. 1-17, 2019.
  7. A. Hotho, A. Nurnberger, and G. Paass, "A brief survey of text mining," LDV Forum, Vol. 20. No. 1, pp. 19-62. 2005. https://doi.org/10.21248/jlcl.20.2005.68
  8. R. L. Li, and C. N. Jun, "An Analysis of the Marketing Environment in the Chinese Glasses Market: Focus on Utilizing of Big Data," Global Business Administration Review, Vol. 15, No. 3, pp. 1-24, 2018. https://doi.org/10.38115/asgba.2018.15.3.1
  9. S. B. Lee and M. H. Song, "An Analysis of the Age-Downing Issues in the Election of Public Official Election Act: Based on the Semantic Network Analysis of Newspaper Articles," The Study of Election, Vol. 1, No. 13, pp. 5-35. 2021. https://doi.org/10.23018/NEC.2020.1.13.001
  10. K. W. Byun, "A Study on the Meaning of The First Slam Dunk Based on Text Mining and Semantic Network Analysis," International Journal of Advanced Smart Convergence Vol. 12, No.1, pp. 164-172. 2023. DOI: http://dx.doi.org/10.7236/IJASC.2023.12.1.164