Comparative Perceptions of Korean Fine Dining: A Big Data Analysis of Michelin-Starred Restaurants in and outside the USA

Abstract

In today’s digital era, millions of diners share their experiences online, creating vast datasets that reveal how consumers perceive global cuisines. This study applies big data text-mining techniques to compare perceptions of Michelin-starred Korean restaurants located in and outside the United States. Using 9,535 Google Maps reviews from 17 restaurants collected through Outscraper, qualitative analysis was performed using KH Coder to identify high frequency keywords and co-occurrence networks. The comparative results revealed clear regional distinctions. Reviews from U.S.-based restaurants emphasized service quality, Michelin prestige, and memorable experiences, reflecting expectations shaped by Western fine-dining culture. In contrast, reviews from restaurants outside the U.S. highlighted authentic flavor, Korean identity, and sensory dining experiences, demonstrating greater cultural curiosity and appreciation for culinary authenticity. These findings indicate that regional contexts significantly shape diners’ cognitive and emotional framing of K-Food, blending dimensions of prestige, authenticity, and experience in unique ways. By visualizing these linguistic and semantic differences, this study expands understanding of cross-cultural gastronomy and online consumer behavior. The results offer theoretical and managerial implications for Korean fine-dining restaurants seeking to adapt branding and communication strategies across diverse global markets.