Tourism recommendation system based on semantic clustering and sentiment analysis

Numerous number of tourism attractions along with a huge amount of information about them on web and social platforms have made the decision-making process for selecting and visiting them complicated. In this regard, the tourism recommendation systems have become interesting for tourists, but challenging for designers because they should be able to provide personalized services. This paper introduces a tourism recommendation system that extracts users' preferences in order to provide personalized recommendations. To this end, users' reviews on tourism social networks are used as a rich source of information to extract their preferences. Then, the comments are preprocessed, semantically clustered, and sentimentally analyzed to detect a tourist's preferences. Similarly, all users aggregated reviews about an attraction are utilized to extract the features of these points of interest. Finally, the proposed recommendation system, semantically compares the preferences of a user with the features of attractions to suggest the most matching points of interest to the user. In addition, the system utilizes the vital contextual information of time, location, and weather to filter unsuitable items and increase the quality of suggestions regarding the current situation. The proposed recommendation system is developed by Python and evaluated on a dataset gathered from TripAdvisor platform. The evaluation results show that the proposed system improves the f-measure criterion in comparison with the previous systems.