In this paper advanced Sentiment Analysis techniques were applied to evaluate public opinions reported by rail users with respect to four major European railway companies, i.e., Trenitalia and Italo in Italy, SNCF in France and Renfe in Spain. Two powerful language models were used, RoBERTa and BERT, to analyze big amount of text data collected from a social platform dedicated to customers reviews, i.e., TrustPilot. Data concerning the four European railway companies were first collected and classified into subcategories related to different aspects of the railway sector, such as train punctuality, quality of on-board services, safety, etc. Then, the RoBERTa and BERT models were developed to understand context and nuances of natural language. This study provides a useful support for railways companies to promote strategies for improving their service.

This research aims to test the effect that the implementation of green practices at a major sport tourism event, the Badminton World Championships in Huelva (Spain), has on the future intention of spectators to return to similar sport events. A total of 523 spectators who attended the event were randomly selected and self-administered in the presence of the interviewer. A confirmatory factor analysis of the model and a multi-group analysis were carried out. Sporting events have a great impact on the environment in which they are organised, mainly when they are linked to tourism, whether at an economic, social or environmental level. The results indicated that green practices indirectly influence spectators' future intentions through emotions and satisfaction, direct antecedents. In addition, green practices directly affect both image and trust, and indirectly affect satisfaction. In conclusion, green practices are a variable to be taken into account when planning the organisation of a sporting event that aims to consolidate itself in the tourism and sports services market.