Profiling Cultural Tourists by Using User Generated Big Data from Online Travel Agencies

Cultural tourism, as one of the most popular forms of tourism, has recently witnessed a remarkable development. However, rapid development of cultural tourism has brought fierce competition. In order to increase the attractiveness of scenic spots, it is very necessary for tourism enterprises to accurately understand cultural tourists‘ preference. This paper proposes a systematic method for profiling cultural tourists based on user generated big data. In this method, topic model, sentiment analysis and clustering algorithms are combined to cluster tourists, and then multinomial logistic regression model are applied to match tourists’ basic attributes. Calculation results show that cultural tourists are mainly divided into four groups with different characteristics. According to the characteristics of each group, suggestions for improving the management of scenic spots are put forward.