Smart tourism destinations are characterized by a pervasive presence of new technologies able to influence and improve the quality of tourist experiences. Understanding how tourists perceive and evaluate the assets and point of interests of a smart tourism destination is the first step for supporting decision makers and urban planners in making coherent and informed decisions aiming at improving the competitiveness of the destination. In this paper, we present a novel approach based on Granular Computing and Rough Set Theory for the analysis of the collective perception of a community of users with respect to points of interest (POI) in a smart tourism destination. The approach supports the classification of the POIs with respect to the tourists' perception by leveraging on the Three-way decision model and Probabilistic Rough Sets. The creation of multi-level granular structures representing the users' opinions is an added value of the approach when dealing with many POIs and large communities of tourists. An illustrative example related to the city of Salerno is reported to demonstrate the capability of the approach in supporting decision makers in the evaluation of tourist experiences.
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