해남관광에 대한 소셜미디어 빅데이터 탐색 연구
A Study on the Exploration of Big Data from Social Media Regarding Haenam Tourism
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초록
This study investigates the recognition of Haenam tourism by analyzing keywords closely associated with the region, extracted from big data collected through major social media platforms in South Korea. To derive meaningful insights, various social media big data analysis methods were applied, including text mining, keyword frequency analysis, and centrality analysis. The key findings are as follows: First, keywords such as Jeonnam, travel, Daeheungsa Temple, Gwaneumjeon Hall, Land’s End, Jindo, tourist destination, Yeongam, recommendation, Land’s End Village, Gangjin, Yeosu, Mokpo, hotel, and festival were frequently mentioned in online discussions, reflecting heightened public interest. Second, centrality analysis revealed that travel and recommendation exhibited high degree centrality, indicating their prominence in the network. In contrast, festival, photo, tourist complex, and experience showed high closeness centrality, implying their function as bridging or connective terms in the discourse network. Finally, the CONCOR analysis identified five thematic clusters: (1) tourist destinations and recommended travel courses, (2) Buddhist cultural tourism, and (3) nature-friendly leisure and stay-based tourism, among others.
Abstract
This study investigates the recognition of Haenam tourism by analyzing keywords closely associated with the region, extracted from big data collected through major social media platforms in South Korea. To derive meaningful insights, various social media big data analysis methods were applied, including text mining, keyword frequency analysis, and centrality analysis. The key findings are as follows: First, keywords such as Jeonnam, travel, Daeheungsa Temple, Gwaneumjeon Hall, Land’s End, Jindo, tourist destination, Yeongam, recommendation, Land’s End Village, Gangjin, Yeosu, Mokpo, hotel, and festival were frequently mentioned in online discussions, reflecting heightened public interest. Second, centrality analysis revealed that travel and recommendation exhibited high degree centrality, indicating their prominence in the network. In contrast, festival, photo, tourist complex, and experience showed high closeness centrality, implying their function as bridging or connective terms in the discourse network. Finally, the CONCOR analysis identified five thematic clusters: (1) tourist destinations and recommended travel courses, (2) Buddhist cultural tourism, and (3) nature-friendly leisure and stay-based tourism, among others.