The Korean Journal of Community Living Science (한국지역사회생활과학회지)

The Korean Society of Community Living Science (한국지역사회생활과학회)
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Study of the Activation Plan for Rural Tourism of the Jeollabuk-do Using Big Data Analysis

빅데이터 분석을 통한 농촌관광 실태와 활성화 방안 연구: 전라북도를 중심으로

  • Park, Ro Un (Dept. of Integrated Bio-Resource Science, General Graduate School of Jeonju University) ;
  • Lee, Ki Hoon (Dept. of Business Administration, Jeonju University)
  • 박로운 (전주대학교 대학원 생명자원융합과학과) ;
  • 이기훈 (전주대학교 경영학과)
  • Received : 2016.10.04
  • Accepted : 2016.10.24
  • Published : 2016.10.31
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Abstract

This study examined the main factors for activating rural tourism of Jeollabuk-do using big data analysis. The tourism big data was gathered from public open data sources and social network services (SNS), and the analysis tools, 'Opinion Mining', 'Text Mining', and 'Social Network Analysis(SNA)' were used. The opinion mining and text mining analysis identified the key local contents of the 14 areas of Jeollabuk-do and the evaluations of customers on rural tourism. Social network analysis detected the relationships between their contents and determined the importance of the contents. The results of this research showed that each location in Jeollabuk-do had their specific contents attracting visitors and the number of contents affected the scale of tourists. In addition, the number of visitors might be large when their tourism contents were strongly correlated with the other contents. Hence, strong connections among their contents are a point to activate rural tourism. Social network analysis divided the contents into several clusters and derived the eigenvector centralities of the content nodes implying the importance of them in the network. Tourism was active when the nodes at high value of the eigenvector centrality were distributed evenly in every cluster; however the results were contrary when the nodes were located in a few clusters. This study suggests an action plan to extend rural tourism that develop valuable contents and connect the content clusters properly.

Keywords

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