# Understanding Customers' Transport Services with Topic Clustering and Sentiment Analysis
## 1 Introduction  
During the last decade, social media has constantly been growing, and it has completely changed the way people communicate and interact with the rest of the world [1]. 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. This led companies to create official accounts on the most popular social media networks in order to help customers with their concerns, questions, and opinions.  
Customers can freely express their satisfaction with a brand, and this could directly affect the brand's popularity since millions of users can read this public information. Moreover, companies need to analyze their competitors to obtain a competitive advantage [2]. For these reasons, the periodic capture and analysis of this large amount of user-generated content will be critical for understanding the public opinion about brands and the provision of higher quality responses that satisfy their needs.  
Previous to this work, there have been several research projects that investigated the analysis of user opinions in ride-hailing services. Some examples have been focused on Facebook [3] and Twitter [4, 5] domains. These research projects have proved that the analysis of brands in this sector can be critical for understanding their customer's opinions to adapt their business models to their customer needs.  
Thus, this article aims to analyze customer service from Twitter. This social network is one of the most popular referring to customer services. Its simplicity of publishing short messages has tailored perfectly with customer needs of communication with brands. We decided to analyze Uber Customer Service (@Uber_Support [6]) due to its fast growth inthe ride-sharing sector [7] and its robust customer service. Furthermore, we are particularly interested in customer replies to the platform. We consider these tweets as the most valuable ones for companies to understand the real necessities of customers when communicating with brands.  
However, the individual analysis of these tweets may not represent the opinions and concerns of the majority of customers. So, in this project, we attempt to seek answers to the following questions:  
1. What are the most discussed topics posted by users in a customer service platform regarding a transport company from Twitter?
2. How do different topic modeling and clustering technologies compare in terms of performance?
3. What are the areas from transport and customer services where user satisfaction needs to be improved?  
To obtain a conclusion to these questions, we will perform several data mining techniques in the captured tweets. These methods include the use of a wide range of topic modeling procedures to determine the most discussed topics of the dataset, _opinion mining_ techniques to analyze the user's sentiment and emotions when tweeting to these platforms, and the use of several _document clustering_ algorithms to group these tweets into the different topics as well as their sentiments and emotions.  
The remainder of this paper is organized as follows. Section 2 reviews the related work. Section 3 describes the methodology used to achieve the objectives proposed. Section 4 presents the results obtained with the dataset created. Finally, Section 5 discusses the conclusions of this work and its possible future outcomes.