소셜 빅데이터를 통해 바라본 스포츠과학 기술에 관한 주요 속성 및 인식 분석
Key Attributes and perceptions of sports science and technology through the Lens of social big data
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
This study aims to analyze trends and key attributes related to sports science and technology through big data analysis. The significance of this research lies in presenting these attributes, trends, and future directions of sports science and technology. The data collection period spanned three years, from September 1, 2021, to August 31, 2024, focusing on unstructured text data from Naver, Google, and Daum. The keyword for data collection was set as “sports science + technology.” The data collection tool used was Textom, a big data collection and analysis solution. The collected data were analyzed using Textom and UCINET 6, performing frequency analysis and TF-IDF analysis for text mining, as well as a CONCOR analysis for social network analysis. The frequency analysis and TF-IDF analysis revealed the top 30 terms related to sports science and technology over the last three years. In addition, the CONCOR analysis grouped the terms into four categories: Sports Science and Education, Sports Science and Creative Convergence, Sports Science and Specialization, and Sports Science and ICT. Through this study, key attributes and trends related to sports science and technology were identified, and meaningful insights were presented based on each cluster.
Abstract
This study aims to analyze trends and key attributes related to sports science and technology through big data analysis. The significance of this research lies in presenting these attributes, trends, and future directions of sports science and technology. The data collection period spanned three years, from September 1, 2021, to August 31, 2024, focusing on unstructured text data from Naver, Google, and Daum. The keyword for data collection was set as “sports science + technology.” The data collection tool used was Textom, a big data collection and analysis solution. The collected data were analyzed using Textom and UCINET 6, performing frequency analysis and TF-IDF analysis for text mining, as well as a CONCOR analysis for social network analysis. The frequency analysis and TF-IDF analysis revealed the top 30 terms related to sports science and technology over the last three years. In addition, the CONCOR analysis grouped the terms into four categories: Sports Science and Education, Sports Science and Creative Convergence, Sports Science and Specialization, and Sports Science and ICT. Through this study, key attributes and trends related to sports science and technology were identified, and meaningful insights were presented based on each cluster.