Exploring China's 5A global geoparks through online tourism reviews: A mining model based on machine learning approach. (January 2021) - Luo, Yuyan; He, Jinjie; Mou, Yu; Wang, Jun; Liu, Tao
Abstract: This study aims to advance the existing research on geoparks by incorporating machine learning models in the analysis of online reviews to provide valuable suggestions for managers in increasing their understanding of the psychological cognition of tourists and evaluating the status of geoparks. A total of 120, 532 online reviews of 24 AAAAA UNESCO Global Geoparks in China were linguistically analyzed through multiple machine learning models of the support vector machine, the improved latent Dirichlet allocation, and the importance–performance analysis (IPA). Results uncovered 10 attributes and 80 elements of attributes. From the identified attributes, four negative attributes were specified, namely travel cost, tour services, well-known degree, and transportation and accommodation. As the performance of these geoparks in terms of experience products and geological knowledge education was insufficient, several actions for geoparks and geotourism were suggested on the basis of IPA. Highlights: Advance the existing research on geoparks through online tourism review. Propose a framework by integrating multiple algorithms for mining geoparks. Specify travel cost, tour services, well-known degree and transportation and accommodation as negative attributes among the 10 attributes for identified geoparks. Geoparks should increase the science education and experience products of geotourism.