Understanding the determinants of satisfaction in P2P hosting is crucial, especially with the emergence of platforms such as Airbnb, which has become the largest platform for short-term rental accommodation. Although many studies have been carried out in this direction, there are still gaps to be filled, particularly with regard to the apprehension of customers taking into account their category. In this study, we took a machine learning-based approach to examine 100,000 customer reviews left

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... the Airbnb platform to identify different dimensions that shape customer satisfaction according to each category studied (individuals, couples, and families). However, the data collected do not give any information on the category to which the customer belongs to. So, we applied natural language processing (NLP) algorithms to the reviews in order to find clues that could help us segment them, and then we trained two regression models, multiple linear regression and support vector regression, in order to calculate the coefficients acting on each of the 6 elementary scores (precision, cleanliness, check-in, communication, location, and value) noted on Airbnb, taking into account the category of customers who evaluated the performance of their accommodation. The results suggest that customers are not equally interested in satisfaction metrics. In addition, disparities were noted for the same indicator depending on the category to which the client belongs to. In light of these results, we suggest that improvements be made to the rating system adopted by Airbnb to make it suitable for each category to which the client belongs to.