Current research in data analysis and marketing has focused on developing adaptive systems that extract data from internet resources, process them, and export recommendations to users. Recommendation systems have great impact both for customers and commercial entities. In the present research we introduce the methodology and results, of a new hybrid multi-criteria hotel recommendation system. According to (Pessemier et al., 2017), it is correct and accurate to address the problem of hotel recommendations using multi-criteria methods, as there are many parameters that users consider important and which should be taken into account for an accurate and efficient final recommendation. Within the methodology we combine three different methods of analysis (MUSA, Sentiment Analysis, Filtering). A variant of WAP method is also used to create a preferential user profile for the system. We end up producing personalized product recommendations to system users, which are commensurate with their preferences. Additionally, the user is able to filter the available alternatives with a selection from a set of standard criteria. The use of the minimum satisfaction threshold, that is calculated using sentiment analysis in customer reviews, guarantees the quality of the recommendations. The new recommendation system uses real reviews and ratings for hotels, as well as static hotel features that have been extracted, using datamining methods, from online hotel reservation platform. Inputs of the system are user choices, based on standard criteria, as well as classification of specific criteria in order to create her preferential model. The evaluation of the recommendation system is done by measuring the accuracy of forecasting of evaluations in a real-user experiment. For the case study, we used data for hotels in the prefecture of Chania, Crete. The main metrics used to evaluate the quality of recommendations are: MSE, RMSE, MAE, MAPE.