## 5 Discussion  
This study exemplifies how social networks are evolving into valuable sources of unstructured data. When appropriately modeled, organizations can extract tactical and strategic information for decision-making without requiring significant investments in development and technology. For example, text mining has gained significant importance in generating value and comprehending public sentiment regarding specific subjects. In this instance, the study concentrates on identifying the primary sources of dissatisfaction among customers in Bogota's public services sector, uncovering that \"Service outages\" stand out as the main concern for the users. The definition of methods for sentiment analysis under the dictionary perspective was conducted to increase accuracy and obtain more robust results aligned with the reality being studied. As a result, the BING and AFINN dictionaries achieved better performance, with 70.9% and 63.6%, respectively. Furthermore, for the NRC dictionary, better results were obtained using pre-defined functions in R specifically designed for this purpose. An important contribution of this research lies in the analysis of the temporal evolution of sentiment and the recognition of how it changes over time, enabling the identification of patterns, trends, and outliers. Finally, the results allow for the identification of key problemsfor the improvement of areas across various service fronts for each company, as well as specific issues to be addressed to enhance processes and improve the user experience with the services provided. The application of topic modeling through the LDA algorithm provided the determination of the optimal number of topics (k). However, the literature consulted lacks detailed guidance on selecting this number, which highlights it as an important aspect of this research. The graphical method provides a useful approximation to the dataset, but it necessitates subsequent analysis of each group of words to evaluate their coherence and relevance to the word's aggrupation. Multiple iterations were performed to evaluate and enhance the results, involving numbers of k close to the identified optimal value. The analysis of topics revealed common themes mentioned by customers of the three companies on Twitter, primarily related to \"Service Outages\" and \"Case Resolution and Information Request\". These topics accounted for 20% and 23% of the entire corpus, with approximately 4.47 thousand and 5.1 thousand tweets, respectively. The \"Others\" category was assigned to only 2249 tweets, less than 10% of the downloaded messages, indicating a good approximation by the LDA algorithm. It is recommended to conduct a more in-depth analysis to identify subtopics within critical topics by extending the study period to more than six months and incorporating an additional layer of topic modeling. When examining the monthly behavior of topics for the companies, a consistent trend in critical topics becomes apparent, as the topics with the highest number of associated tweets remain the same month after month. For the aqueduct company, \"Water Truck Request\" and \"Service Outages\" stand out, while for Enel, failures or claims for \"Service Outages\" represent the most frequent problem that needs to be solved. For Vanti, \"Case Resolution and Information Request\" takes precedence. Therefore, it is recommended to conduct a more comprehensive analysis to identify subtopics within each critical topic, extending the study period beyond six months and employing an additional layer of topic modeling. Heatmaps used to identify critical topics for each company proved to be effective visual tools for promptly recognizing customer concerns. The analysis indicates that the overall perception is not favorable, as negative tweets have increased. \"Service Outages\" emerged as the critical topic for Acueducto and Enel, while Vanti experienced the most significant issues related to \"Case Resolution and Information Request\".