# Understanding Customers' Transport Services with Topic Clustering and Sentiment Analysis
## 5 Conclusions and Outlook  
This work has focused on analyzing the messages posted by users to a customer service platform based on the social network Twitter. Specifically, we have analyzed the customer's voice from the @Uber_Support transport service platform during the complete year of 2020. Furthermore, three main research questions have driven this work. In relation to the first research question, before obtaining the different topics from the dataset, we first have characterized a global analysis of the collected data, which helped us to understand the users' viewpoints when publishing information in the context of both  
Figure 9: Sentiments and emotions associated with each topic. transport and customer services. Specifically, we have been able to quantify the monthly volume of tweets in which we have determined that the COVID-19 disease directly affected the daily volume of questions and concerns of users towards these services. Additionally, we have been able to characterize the sentiments and emotions of the users when tweeting to this customer service platform. The conclusion we draw from this analysis was that a wide range of users who post negative tweets usually relates to disgust and anger expressions. Furthermore, we noticed that the majority of the tweets containing sentiments usually describe similar vocabulary expressions, which can be related to the context of both transport and customer services. However, the previous analysis did not characterize the underlying structure of the dataset since it only described the most common statistics from an opinion mining project. Therefore, to provide high-quality approaches that can help companies to understand users' demands, we considered the importance of answering our first research question related to obtaining the latent topics in the platform. For this reason, we performed several pre-processing techniques on the collected data, and we combined them with several topic modeling techniques. This process concluded that customers and drivers usually posted in 2020 tweets to @Uber_Support concerning seven different topics. In addition to the topic modeling system, we performed several clustering techniques to group each tweet into the identified topics. Firstly, we introduced into the project the K-Means algorithm, where we concluded that the optimal number of clusters to divide the space referred to seven clusters. Additionally, intending to optimize the solution given by the K-Means approach, we used a Genetic Algorithm based on clustering LDA probability vectors following the model proposed in [15]. Finally, we introduced the fittest individual from the previous algorithm into a local convergence algorithm which followed the linear restrictions imposed by the probability simplex for each cluster. The conclusions obtained from both topic modeling (NMF, LDA, and LSI) and clustering algorithms (K-Means, GA, and hybridized GA) performances helped us to provide an answer to our second research question, which is intimately related to the project's optimization. In the case of our dataset, we concluded that the combination of LDA with the K-Means algorithm from the Scikit-learn library [35] or the hybridized GA describes the best performance results among all the algorithms used. Finally, the combination of both topic modeling and clustering techniques helped us to dissect the high volume of data in a detailed description of the sentiment and emotion information associated with each topic. The obtained results indicated that some specific topics related to time policies, food delivery, and issues related to logging or validation of the account described a high volume of users' negative tweets. This worrying outcome suggests that some users may have lost their loyalty towards Uber's services which could result in the use of Uber's competence to satisfy their needs. Moreover, this analysis helped us to explore in detail the impact of the emotions combined with the sentiment description for each of the different topics.