빅데이터 감성분석을 통한 프로농구 발전 방안 모색

Abstract

The purpose of this study was to find the current spectators’ perception of Korean Basketball League(KBL) through big data analysis, and to find development plans for KBL based on that. For this purpose, the data was collected from Naver Blog and Naver Cafe by using Textom software with the key words(professional basketball, KBL). In data mining analysis, the key words related to KBL was drawn through frequency analysis and TF-IDF. In addition, in semantic network analysis, the relations between key words and sentiments were visualized through key words-sentiment network analysis. The results from the above study methods are as follows. First, the quantity of the buzz collected from the preparation period was much less than the quantity of the buss collected from the season period. Second, the results of data mining analysis showed different patterns in preparation period and season period, and the season and the whole period showed a similar pattern. Third, in the semantic network analysis, it was found that neutral emotion is highly correlated with athlete, and referee. Positive emotion is highly correlated with salary, athlete, Seo, Jang-hun, and negative emotion is derived form referee and Jung-hyun Lee. Based on the above results, this study suggests development of star players, utilization of retired players, utilization of media, and expansion of social contribution activities. Also, it proposes changing the referee system, the establishment of the video assistant referee system and the formation of the fan committee.