A model to measure tourist preference toward scenic spots based on social media data: A case of Dapeng in China

Research on tourist preference toward different tourism destinations has been a hot topic for decades in the field of tourism development. Tourist preference is mostly measured with small group opinion-based methods through introducing indicator systems in previous studies. In the digital age, e-tourism makes it possible to collect huge volumes of social data produced by tourists from the internet, to establish a new way of measuring tourist preference toward a close group of tourism destinations. This paper introduces a new model using social media data to quantitatively measure the market trend of a group of scenic spots from the angle of tourists' demand, using three attributes: tourist sentiment orientation, present tourist market shares, and potential tourist awareness. Through data mining, cleaning, and analyzing with the framework of Machine Learning, the relative tourist preference toward 34 scenic spots closely located in the Dapeng Peninsula is calculated. The results not only provide a reliable "A-rating" system to gauge the popularity of different scenic spots, but also contribute an innovative measuring model to support scenic spots planning and policy making in the regional context.