  Recently, the application of Artificial Intelligence algorithms in hotel
recommendation systems has become an increasingly popular topic. One such
method that has proven to be effective in this field is Deep Learning,
especially Natural Language processing models, which are able to extract
semantic knowledge from user's text reviews to create more efficient
recommendation systems. This can lead to the development of intelligent models
that can classify a user's preferences and emotions based on their feedback in
the form of text reviews about their hotel stay experience. In this study, we
propose a Natural Language Processing framework that utilizes customer text
reviews to provide personalized recommendations for the most appropriate hotel
based on their preferences. The framework is based on Bidirectional Encoder
Representations from Transformers (BERT) and a fine-tuning/validation pipeline
that categorizes customer hotel review texts into "Bad," "Good," or "Excellent"
recommended hotels. Our findings indicate that the hotel recommendation system
we propose can significantly enhance the user experience of booking
accommodations by providing personalized recommendations based on user
preferences and previous booking history.
