Predicting A New Hotel Rating System by Analysing UGC Content from Tripadvisor: Machine Learning Application to Analyse Service Robots Influence

作     者：Jorge Calero-Sanz Alicia Orea-Giner Teresa Villacé-Molinero Ana Muñoz-Mazón Laura Fuentes-Moraleda 

作者机构：Signal and Communications Theory and Telematic Systems and Computing Rey Juan Carlos University Madrid Spain Department of Applied Mathematics and Statistics EIAE Technical University of Madrid Madrid Spain High Performance Research Group OPENINNOVA Rey Juan Carlos University Madrid Spain EIREST Université Paris 1 Panthéon-Sorbonne Paris France Business economics Rey Juan Carlos University Madrid Spain Centro Universitario de Estudios Turísticos Rey Juan Carlos University Madrid Spain 

出 版 物：《Procedia Computer Science》 

年 卷 期：2022年第200卷

页      面：1078-1083页

主　　题：robot typology traveler TripAdvisor Rating Machine Learning 

摘      要：Industry 4.0 tools permit computerized creation measures, and Artificial Intelligence (AI) approaches are pivotal in investigating the travel industry. Applying these devices to decipher User Generated Content (UGC) is fundamental to understand better client’s necessities, opinions, and assumptions regarding tourism services. Through this research, an exploratory analysis of results is developed through Machine Learning Models to understand better the role played by robot and traveler typologies on the rating given to hotels considering TripAdvisor reviews of 74 hotels. The purpose of this exploratory research is to develop a methodology focused on analyzing online reviews related to service robots in hotels using Machine Learning techniques to train the data collected from TripAdvisor. Preliminary results show a link between the hotel rating given in TripAdvisor and the robot typology implemented in hotels.