Analysis of Keyword Trends for Park Golf Based on Social Network Data

Abstract

The purpose of this study was to analyze park golf-related keywords based on social network data and present park golf trends. Park golf keywords were collected through the crawling process from the Naver News and Blog portals. Python package BeautifulSoup was utilized for crawling, and keyword analysis was conducted using KoNLPy and CountVectorizer for text mining. Furthermore, Document Term Matrix (DTM) was employed to analyze four centralities(connection centrality, betweenness centrality, closeness centrality, and eigenvector centrality). The results were visualized using Network X, yielding the following outcomes.BR Firstly, among the park golf-related keywords, the term ‘golf course’, had the highest frequency, followed by ‘composition’, ‘park, ‘project’, ‘facilities’, ‘hole’, ‘region’, ‘plan’, ‘usage’, and ‘sports’. Secondly, the structure of park golf-related keywords showed that ‘park’, ‘creation’, ‘facilities’, and ‘project’ had relatively high centrality indices.BR As a result, the comprehensive analysis of keywords related to park golf has led to the conclusion that park golf course site selection and development, as well as facility management, have become a prevailing trend. Even the park golf-related business has taken shape as a trend. To promote the activation of park golf, strategic marketing approaches should be implemented, including operational plans through the construction of multifunctional park golf courses, expansion of site areas through private park golf course development, and fostering the park golf industry through the growth of equipment, tour competitions, and coaching.