###### Abstract  
The main goal of this research is to analyze the perception of service in public sector companies in the city of Bogota via Twitter and text mining to identify areas, problems, and topics aiming for quality service improvement. To achieve this objective, a structured method for data modeling is implemented based on the KDD methodology. Tweets from January to June 2022 related to the companies in the sector are processed, and a temporal analysis of the evolution of sentiment is performed based on the dictionaries Bing, AFINN, and NRC. Subsequently, the LDA algorithm (Latent Dirichlet Allocation algorithm) is used to visually identify the topics with the greatest negative impact reported by the users in each of the 6 months by adding the temporal dimension. The results revealed that, for Aquet (water supply service), the topic with the highest dissatisfaction is related to the \"Water Tank Request\" processes; for Enel (energy services) \"Service Outages\"; and for Vanti (gas services), \"Case solution and request information\". Temporal patterns of tweets, sentiments, and topics are also highlighted for the three companies.  
LDA; topic modeling; Twitter; temporal analysis; sentiment analysis Article  
Monitoring the Quality and Perception of Service in Colombian Public Service Companies with Twitter and Descriptive Temporal Analysis April 2023130380  
instance, Enel reported in 2020 a growth in publications, followers, and comments of more than 147% compared to 2019 [5].  
Similar case studies demonstrate the feasibility and utility of this type of analysis for real-life applications in several fields. It is possible to find in the scientific literature some contributions that aim to achieve a similar goal as our study. A couple of examples can be listed. In [6], a study uses different classification methods (e.g., decision trees) to categorize customer comments based on their experience with tourism companies. In [7], research proposed how to detect problems and label opportunities for improvement processes. This was obtained from the comments of customers in the retail sector of some pharmaceutical companies on Twitter through the use of topic modeling, sentiment analysis, and visualization tools.  
Regarding Bogota public service providers, there are no references focused on monitoring the quality and perception of service via Twitter. Our study intends to cover this gap, and at the same time, it incorporates descriptive temporal analysis to get a more dynamic overview related to the voice of the customers that interact with the three most important companies of public services in Bogota.  
The potential of the voice of customers using Twitter as a digital customer service channel with its temporal patterns and evolution is the basis of this research, which integrates not only classical text mining and topic modeling but also the temporal dimension that enriches the results and facilitates their interpretability as support for intelligent decision-making.  
The document is presented as follows: a first section where the main scientific background is exposed. This section is divided into subsections to better describe the state of the art according to the key concepts addressed in this research. The second section contains the material and methods. Then, the results of the temporal and trend analysis overview are presented. Additionally, the application of topic modeling (LDA), the critical topics, and their temporal evolution are determined. Finally, conclusions and future works are listed to close the article.