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Monitoring the Quality and Perception of Service in Colombian Public Service Companies with Twitter and Descriptive Temporal Analysis

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Monitoring the quality and perception of service in Colombian public service companies.pdf (11.36 MB)
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Conti, Dante
Gomez, Carlos Eduardo
Jaramillo, Juan Guillermo
Ospina, Victoria Eugenia
Abstract (Spanish)
El objetivo principal de esta investigación es analizar la percepción del servicio en las empresas del sector público de la ciudad de Bogotá a través de Twitter y minería de textos para identificar áreas, problemas y temas tendientes a mejorar la calidad del servicio. Para lograr este objetivo se implementa un método estructurado para el modelado de datos basado en la metodología KDD. Se procesan los tuits de enero a junio de 2022 relacionados con las empresas del sector y se realiza un análisis temporal de la evolución del sentimiento a partir de los diccionarios Bing, AFINN y NRC. Posteriormente, se utiliza el algoritmo LDA (Algoritmo Latent Dirichlet Allocation) para identificar visualmente los temas con mayor impacto negativo reportados por los usuarios en cada uno de los 6 meses agregando la dimensión temporal. Los resultados revelaron que, para Acueducto (servicio de abastecimiento de agua), el tema con mayor insatisfacción está relacionado con los procesos de “Solicitud de Tanque de Agua”; para Enel (servicios energéticos) “Cortes del Servicio”; y para Vanti (servicios de gas), “Solución de caso y solicitud de información”. También se destacan los patrones temporales de tweets, sentimientos y temas de las tres empresas.
Abstract (English)
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 Aqueduct (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.
Extent
25 páginas
URI: https://repositorio.escuelaing.edu.co/handle/001/3158
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