텍스트마이닝 기반 스포츠클럽 언론보도 분석 : 정책·제도 현황과 개선 방향 = Text Mining-Based Analysis of Sports Club News Coverage : Policy and Institutional Status and Improvement Strategies

다국어 초록 (Multilingual Abstract)

This study uses text mining of news articles on sports clubs to examine policy and institutional issues and suggest improvements. Using Textom and UCINET 6, a total of media reports published between January 2018 and June 2025 containing the terms "public sports club," "registered sports club," or "designated sports club" were collected and analyzed. The analysis employed frequency analysis, TF-IDF, semantic network analysis, and CONCOR clustering to extract key terms and identify discourse patterns. The most frequently occurring keywords included "sports," "club," "public," "athlete," "physical education," "registration," "facility," "competition," and "designation." Based on CONCOR analysis, four distinct discourse clusters were identified: (1) community-based public cooperation sports clubs, (2) designated sports clubs for elite athlete development, (3) promotion of student-centered sports club competitions, and (4) concentration of elite athlete registration in specific sports disciplines. Therefore, to strengthen sports clubs’ public interest and self-sufficiency and ensure sustainable community-based operations, it is essential to build regional sports governance, enhance community functions, and close institutional gaps for registered clubs.