텍스트 마이닝을 활용한 미용실 방문객 온라인 리뷰 분석

Recently, the beauty industry has been rapidly evolving with the rise of shared hair salons, which support individual entrepreneurship and offer flexible working environments. Unlike traditional franchise salons that operate under fixed organizational structures, shared salons provide customers with new service experiences, leading to changes in consumer satisfaction and perception. As internet usage expands, consumers increasingly rely on user-generated evaluations, making online reviews a crucial element in marketing strategies. In the hair industry, where visual impact plays a major role, online marketing is particularly prevalent. Because online reviews reflect personal experiences, they offer valuable insights for corporate marketing. This study aims to analyze consumer perceptions of shared versus franchise hair salons and to extract representative topics associated with each salon type to better understand consumer responses. To achieve this, online reviews posted on Naver for both shared and franchise hair salons were collected and analyzed using text mining techniques, including Latent Dirichlet Allocation (LDA) topic modeling. The analysis yielded six distinct topics. The results indicate that visitors to shared salons value the skills and practicality of individual designers, whereas visitors to franchise salons emphasize structured processes and service quality. These findings suggest that shared and franchise salons offer different types of customer experiences. Based on these insights, the study proposes that shared salons should adopt more standardized systems and services, while franchise salons may benefit from personalized one-on-one service marketing strategies.