비정형 빅데이터 분석을 활용한 무장애 관광 인식 탐색 연구
A Study on the Recognition of Barrier-Free Tourism Utilizing Unstructured Big Data Analysis
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Barrier-free tourism seeks to establish a more accessible and inclusive travel environment by eliminating physical and psychological obstacles and enhancing convenience for socially vulnerable groups such as the elderly, persons with disabilities, pregnant women, and families with young children. This study aims to analyze keywords related to barrier-free tourism by collecting and examining discourse from social media and associated online platforms, in order to gain a comprehensive understanding of public awareness regarding barrier-free tourism. The methodology of this study applies semantic network analysis. The results of the study are as follows. First, keywords such as travel, tourist destination, business, culture, and center were frequently discussed online. Second, centrality analysis revealed that keywords such as Tourism Organization, forest, and accessibility demonstrated high degree centrality, whereas keywords like tourist destination, reinforcement, and product exhibited relatively low closeness centrality. Finally, based on the results of CONCOR analysis, seven clusters were identified, including experiential tourism programs, enhanced accessibility and information, eco-tourism sites, and convenience-related infrastructure.
Abstract
Barrier-free tourism seeks to establish a more accessible and inclusive travel environment by eliminating physical and psychological obstacles and enhancing convenience for socially vulnerable groups such as the elderly, persons with disabilities, pregnant women, and families with young children. This study aims to analyze keywords related to barrier-free tourism by collecting and examining discourse from social media and associated online platforms, in order to gain a comprehensive understanding of public awareness regarding barrier-free tourism. The methodology of this study applies semantic network analysis. The results of the study are as follows. First, keywords such as travel, tourist destination, business, culture, and center were frequently discussed online. Second, centrality analysis revealed that keywords such as Tourism Organization, forest, and accessibility demonstrated high degree centrality, whereas keywords like tourist destination, reinforcement, and product exhibited relatively low closeness centrality. Finally, based on the results of CONCOR analysis, seven clusters were identified, including experiential tourism programs, enhanced accessibility and information, eco-tourism sites, and convenience-related infrastructure.