During the last decade, social media information has experienced constant growth. This fact is largely due to the increment of users using these platforms and the amount of data and relevant information about them. Moreover, social media has completely changed the way people communicate and interact with the rest of the world. This impact has directly affected customer engagement. Some of the most important companies have decided to modify their customer service model based on phone calls and paper forms to be adapted to this new way of communicating between users and companies. Keeping this in mind, this project aims to determine if it is possible to dissect a customer service on the Twitter social network. Specifically, we will focus on analysing the questions and concerns that users from the Uber transport service have. To do this, we have created a dataset containing tweets from the English speakers addressing the @Uber Support platform during the year 2020. Firstly, we performed a global analysis of the collected data. This first approach helped us to understand how users publish information in the context of both transport and customer services. Secondly, to characterize the dataset’s underlying structure, we implemented a Topic Modelling System combined with a Topic Clustering one based on a hybridized Genetic Algorithm. The performance of both systems described a result of forming seven groups of topics in order to cluster the different tweets from the dataset efficiently. Finally, the combined implementation of both systems also allowed us to characterize the polarity associated with each topic, giving us the complete stance of users towards the specific issues described in the platform. To conclude, we believe that the systems created in the project can be efficiently used for the periodic analysis of the information on social media. Thus, companies who require public opinions to establish marketing approaches can easily handle this high user-generated content by using the systems described.