Abstract

User satisfaction is an important aspect to consider in any public transport system, and as such, regular and sound measurements of its levels are fundamental. However, typical evaluation schemes involve costly and time-consuming surveys. As a consequence, their frequency is not enough to properly and timely characterize the satisfaction of the users. In this paper, we propose a methodology, based on Twitter data, to capture the satisfaction of a large mass of users of public transport, allowing us to improve the characterization and location of their satisfaction level. We analyzed a massive volume of tweets referring to the public transport system in Santiago, Chile (Transantiago) using text mining techniques, such as sentiment analysis and topic modeling, in order to capture and group bus users' expressions. Results show that, although the level of detail and variety of answers obtained from surveys are higher than the ones obtained by our method, the amount of bus stops and bus services covered by the proposed scheme is larger. Moreover, the proposed methodology can be effectively used to diagnose problems in a timely manner, as it is able to identify and locate trends, and issues related to bus operating firms, whereas surveys tend to produce average answers. Based on the consistency and logic of the results, we argue that the proposed methodology can be used as a valuable complement to surveys, as both present different, but compatible characteristics.