텍스트 마이닝을 활용한 국내 운동선수 회복 연구 동향 분석: 2010년~2024년 중심으로

초록

This study analyzed 170 articles on athlete recovery published in Korean academic journals between 2010 and 2024 using text mining techniques to identify research trends and future directions. Methods included yearly and society-based distributions, word frequency and TF-IDF analyses, semantic network analysis, LDA topic modeling, and CONCOR analysis. Results showed that 65% of the articles were published in the past five years (2020–2024), indicating growing academic interest. Key terms such as “recovery,” “resilience,” “heart rate,” and “lactate” highlighted both physiological and psychological themes. LDA revealed three topics—physiological recovery and training, game-related factors, and psychological–emotional recovery—while CONCOR produced four clusters: physiological recovery and performance management, training and game management, psychological factors, and injury management with resilience. These findings demonstrate a shift in domestic recovery research from fragmented approaches toward multidimensional and integrative perspectives. Future studies should emphasize practical applications, such as sport-specific recovery indices and field use of psychological recovery tools. This study is limited by its focus on domestic literature and traditional physiological indicators, excluding molecular and genetic-level approaches. Broader, multilayered analyses incorporating international data are therefore needed.

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