How Can Big Data Support Smart Scenic Area Management? An Analysis of Travel Blogs on Huashan

Data-mined user-contributed travel logs on the Mafengwo and Ctrip websites revealed a multi-destination pattern of tourist movements and emotional analysis indicated tourist satisfaction levels, while content analysis explored more deeply into dissatisfying aspects of tourist experiences.

Abstract

Data from travel blogs represent important travel behavior and destination resource information. Moreover, technological innovations and increasing use of social media are providing accessible ‘big data’ at a low cost. Despite this, there is still limited big data analysis for scenic tourism areas. This research on Huashan (Mount Hua, China) data-mined user-contributed travel logs on the Mafengwo and Ctrip websites. Semantic analysis explored tourist movement patterns and preferences within the scenic area. GIS provided a visual distribution of blogger origins. The relationship between Huashan and adjoining tourism areas revealed a multi-destination pattern of tourist movements. Emotional analysis indicated tourist satisfaction levels, while content analysis explored more deeply into dissatisfying aspects of tourist experiences. The results should provide guidance for scenic areas in destination planning and design.