Cultural Tourism Content Factors in Cultural Cities through Text Mining: Focusing on Seogwipo City

Purpose: The purpose of this study was to examine the factors affecting cultural tourism content in cultural cities and to broaden our understanding of the relationship between cultural cities and cultural tourism. Methods: This study conducted text mining on social media big data using 'Seogwipo Cultural City' as a search term. TF-IDF (Term Frequency - Inverse Document Frequency) analysis, Centrality Analysis, Structural Equivalence Analysis, and LDA Topic Modelling were used to analyze the social media data. Results: As a result of the analysis, it was confirmed that it is possible to distinguish between culture cluster keywords such as ‘noji culture’ and ‘bookstore day’ through Structural Equivalence Analysis and tourism cluster keywords such as ‘travel’, ‘tourism’, ‘course’, and ‘space’. In addition, through topic modelling, topics related to cultural tourism such as 'bookstore culture contents' and 'Seogwipo Cultural City Art Festival' were classified, and cultural tourism contents were derived. Conclusion: The derivation of cultural tourism content through text mining was a method of exploring cultural tourism content factors after the establishment of a cultural city, which was different from the existing method of considering tourism from the stage of promoting a cultural city. This reactive exploration of cultural tourism content, which derived themes for cultural tourism based on cultural cities, could suggest the possibility of coexistence of cultural tourism with the establishment of local cultural ecosystems in cultural cities. In addition, this approach was significant in that it could dispel concerns about various social problems arising from the touristification of cities.