Profiling Gastronomic Tourists: Clustering Analysis Based on Perceived Korean Food Experiences
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This study investigates the heterogeneity of gastronomic tourists by analyzing how individuals perceive and discuss Korean food in English-language YouTube contents. Using topic proportions derived from Structural Topic Modeling (STM), clustering analysis was applied to identify naturally occurring tourist segments based on thematic emphasis. Seven distinct food tourist profiles emerge: Cultural food explorers, Flavorful experience seekers, BBQ and grill enthusiasts, Casual comfort food seekers, Traditionalists, Scenic experience seekers, and Adventurous diners. Each cluster reflects unique preferences and cultural orientations in food-related travel behavior. This study contributes to tourism research by integrating computational text analysis with segmentation theory, and offers practical implications for designing personalized culinary experiences and targeted marketing strategies within Korea's dynamic food tourism landscape.
Abstract
This study investigates the heterogeneity of gastronomic tourists by analyzing how individuals perceive and discuss Korean food in English-language YouTube contents. Using topic proportions derived from Structural Topic Modeling (STM), clustering analysis was applied to identify naturally occurring tourist segments based on thematic emphasis. Seven distinct food tourist profiles emerge: Cultural food explorers, Flavorful experience seekers, BBQ and grill enthusiasts, Casual comfort food seekers, Traditionalists, Scenic experience seekers, and Adventurous diners. Each cluster reflects unique preferences and cultural orientations in food-related travel behavior. This study contributes to tourism research by integrating computational text analysis with segmentation theory, and offers practical implications for designing personalized culinary experiences and targeted marketing strategies within Korea's dynamic food tourism landscape.