빅데이터 분석을 통한 학생선수에 관한 개념과 속성 탐색

Abstract

The purpose of this study was to verify the concept and perception of student athlete through social media big data anlaysis, and to suggest value and direction of policy and system for student athletes. The data collection period was from January 1, 2017 to June 30, 2022, focusing on the keyword of “student athlete” for unfiltered and unstructured text data appearing on Naver, Daum, Google, and YouTube. Textom, a big data collection and analysis solution, was used for data collection. For data analysis, frequency analysis and TF-IDF (Term Frequency Inverse Document Frequency) analysis of text mining analysis were performed, and connection centrality and CONCOR (CONvergence of iterated CORrelations) analysis of social network analysis were performed using Textom and UCINET 6.0 program. The results of this study were as follows. First, the total amount of textual data collected through social media was 5,424 cases (2,743KB). Second, as a result of frequency and TF-IDF analysis, ‘athlete’, ‘student’, ‘student athlete’, ‘competitions’, ‘sports’ had the most in common. Third, As a result of the connection centrality analysis, centrality measures were shown in the order of athletes (374.220), students (288.441), student athletes (175.441), competitions (120.373), and exercise (99.949). Lastly, as a result of CONCOR analysis, it was categorized into seven clusters: ‘player training system’, ‘academic parallelism’, ‘identity’, ‘policy support’, ‘career / college-preparatory’, ‘research activities’, and ‘social issues’. Through these results of such big data analysis, it is possible to provide the academic basic data necessary for proposing policies and strategic directions and improvement measures necessary for the growth of student athletes.