빅데이터를 활용한 코로나19 시기의 홈트레이닝 발전경향 분석

Abstract

Purpose: This study analyzes big data to suggest the development tendency and marketing strategies of home training, which is growing rapidly due to the COVID-19 that started in 2020. Method: To this end, text-mining, tf-idf, connection degree centralyit and semantic network analysis were conducted by using textom, a social matrix program, and Ucinet for network analysis. The data analysis period was selected for six quarters from January 1, 2020 to June 30, 2021. Results: The main analysis results of the study were as follows. First, home training’s word frequency, tfidf, and connection degree centrality analysis quarterly showed that exercise, home training, home workout, and diet words were always in the top 10, but the bottomi xs words in the six quarters showed di-f ferent tendencies. This shows that the perception of home training varies by quarter during the COVID-19 period. Second, as a result of quarterly home training semanti cnetwork analysis, the word composition of both the home training and home training trend groups were diferent. This shows that home training showed different development trends for each quarter during the COVID-19 period. Conclusion: Therefore, the purpose is to provide practical data based onth e analyzed results, and to present a strategy to promote the development of the home training market at the theoretical level.