스포츠 시장가치망 분류에 따른 파생시장이 일자리 창출에 미치는 영향 : 소셜데이터와 PLS(partial least squares regression) 회귀분석을 중심으로

Abstract

Purpose: In this study, using the INSIGHT Deep MiningG program, frequency of mentioning and Emotion-Keyword are compared on a percentage basis to analyze the effect of a derived market of demand-oriented market value network classification on the creation of sports-related jobs. Method: Based on the preceding factors derived from social data analysis, the following conclusions were reached by using the method, partial least-square (PLS), to determine what influence derivative market has on the critical attributes of job creation. The study used the INSIGHT Deep MiningG program to analyze the impact of derived markets on the creation of sports-related jobs by using the demand-oriented market value chain classification as a percentage of social data references and emotional keywords. The following conclusions were reached using the PLS (PLS) regression method to determine the impact of job creation. Results: The results of this study are as follows. First, social users had the highest interest in the sports information market and the sports service market, given the amount of mention in the sports derivatives market of social data. Regarding sports job factors, interest in recruiting and job information was high. Second, the Emotion-Keyword of the sports derivative market showed high positive sentiment corresponding to the image of the new growth industry, while the Emotion-Keyword of the sports job showed negative sentiment due to the anxiety caused by issues such as unfair recruitments. Third, derivative market in the market value network mostly affected sports jobs. Conclusion: Job seekers prefer to work in the sports information market, sports service market, sports athlete training market, and sports tourism market, but the actual employment status is reflected in the sports information market, sports service market, sports tourism market, and sports facility market.