Social big data analysis on demands for the Korean sport industry

Abstract

The purpose of the study was to explore public opinions and perceptions with regard to the Korean sport industry based on a big data analysis of social media content. Social big data was collected using ‘TextoM’, a big data analysis solution and ‘Naver’, an Internet portal service provider. In order to collect data, a total of 29 keywords were used such as sport industry, sport facilities, sport services, sport marketing, professional baseball, and sport manufacturing. Social analytics was used to analyze big data from social media content about the Korean sport industry. A total of 6,002,666 cases including document, web, blogs, and cafes (i.e., online community) with regards to the Korean sport industry were analyzed. Frequency analysis, keyword analysis through text-mining, sentiment analysis, semantic network analysis, and CONCOR network analysis were conducted. First, frequency analysis shows the volume of the Korean sport industry related searches stayed highly and increased. Second, keyword analysis through text mining was conducted to examine frequently used keywords in the sport industry. The analysis indicates four different categories of the Korean sport industry keywords such as sport games, sport goods, health, and sport policy. Third, sentiment analysis showed positive sentiment was found to have a greater weight than negative sentiment toward the sport industry. Fourth, semantic network analysis showed that the Korean sport industry connected with tourism, media, information, game, international, health, and culture. Last, the results of CONCOR analysis showed that the sport industry clearly divided into four different segments such as sport goods and equipment segment, sport facility segment, sport service segment, and professional sport segment. Results and findings are discussed and future research are suggested.