Abstract
Insights into the association rules of destinations can help to understand the possibility of tourists visiting a destination after having traveled from another. These insights are crucial for tourism industries to exploit strategies and travel products and offer improved services. Recently, tourism-related, user-generated content (UGC) big data have provided a great opportunity to investigate the travel behavior of tourists on an unparalleled scale. However, existing analyses of the association of destinations or attractions mainly depend on geo-tagged UGC, and only a few have utilized unstructured textual UGC (e.g., online travel reviews) to understand tourist movement patterns. In this study, we derive the association of destinations from online textual travel reviews. A workflow, which includes collecting data from travel service websites, extracting destination sequences from travel reviews, and identifying the frequent association of destinations, is developed to achieve the goal. A case study of Yunnan Province, China is implemented to verify the proposed workflow. The results show that the popular destinations and association of destinations could be identified in Yunnan, demonstrating that unstructured textual online travel reviews can be used to investigate the frequent movement patterns of tourists. Tourism managers can use the findings to optimize travel products and promote destination management.
Highlights
IntroductionSpace, place, and scale, which are the basic elements of tourism geography
Spatial movement is an essential behavior of tourism activities
Tourist travel behavior can potentially imply the popularity of tourist attractions and the correlation among destinations
Summary
Space, place, and scale, which are the basic elements of tourism geography. Tourist travel behavior can potentially imply the popularity of tourist attractions and the correlation among destinations. Tourist movement patterns have been an important research topic in tourism geography. Traditional approaches in investigating tourist movement patterns and destination characteristics usually utilize questionnaires, but the collection of this dataset is costly and time consuming [1]. This method is limited in sample size and space–time resolution, making the analysis of tourist travel behavior from a comprehensive and broad perspective difficult.
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