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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.
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