A Study on the Status and Recognition of SPORTS Welfare Using Big Data Analysis
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Purpose: To identify public Recognition and areas of interest in sports welfare through big data related analyti-cal techniques, and to provide basic data required for establishing sports welfare policies. Method: Keyword frequency analysis, sentiment analysis, semantic network analysis,, and CONCOR analysis were performed. Results: The public was demonstrated the highest frequency and derived as a key keyword. As keywords related to the benefit from sports welfare support, health, physical strength, and happiness were derived, and words re-lated to the welfare of athletes such as athletes, competitions, events, games, and leaders appeared. As a result of sentiment analysis, the positive rate(79.52%) was found to be very high. Third, as a result of the semantic network analysis, keywords such as the public, health, sports activities, support, and service are located in the center of the network, and are confirmed as major keywords. As a result of the CONCOR analysis, ‘National Fitness 100’, ‘Sports participation Voucher’, and ‘Participation Policy of Sports for all ’ were formed. Conclusion: The public recognition and areas of interest in sports welfare were examined and Identified based on the results of this research. That could provide the basic data required for devising the sports welfare policies.
Abstract
Purpose: To identify public Recognition and areas of interest in sports welfare through big data related analyti-cal techniques, and to provide basic data required for establishing sports welfare policies. Method: Keyword frequency analysis, sentiment analysis, semantic network analysis,, and CONCOR analysis were performed. Results: The public was demonstrated the highest frequency and derived as a key keyword. As keywords related to the benefit from sports welfare support, health, physical strength, and happiness were derived, and words re-lated to the welfare of athletes such as athletes, competitions, events, games, and leaders appeared. As a result of sentiment analysis, the positive rate(79.52%) was found to be very high. Third, as a result of the semantic network analysis, keywords such as the public, health, sports activities, support, and service are located in the center of the network, and are confirmed as major keywords. As a result of the CONCOR analysis, ‘National Fitness 100’, ‘Sports participation Voucher’, and ‘Participation Policy of Sports for all ’ were formed. Conclusion: The public recognition and areas of interest in sports welfare were examined and Identified based on the results of this research. That could provide the basic data required for devising the sports welfare policies.
참고문헌 (31)
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