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

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 impl...

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Autores:: Conti, Dante
Gomez, Carlos Eduardo
Jaramillo, Juan Guillermo
Ospina, Victoria Eugenia

Tipo de recurso:: Article of journal

Fecha de publicación:: 2023

Institución:: Escuela Colombiana de Ingeniería Julio Garavito

Repositorio:: Repositorio Institucional ECI

Idioma:: eng

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dc.title.eng.fl_str_mv	Monitoring the Quality and Perception of Service in Colombian Public Service Companies with Twitter and Descriptive Temporal Analysis
title	Monitoring the Quality and Perception of Service in Colombian Public Service Companies with Twitter and Descriptive Temporal Analysis
spellingShingle	Monitoring the Quality and Perception of Service in Colombian Public Service Companies with Twitter and Descriptive Temporal Analysis Servicios públicos - Bogotá (Colombia) Public utilities - Bogotá (Colombia) Análisis de la información Information analysis Minería de datos - Bogotá (Colombia) Data mining - Bogotá (Colombia) Extracción de opinión Opinion extraction Minería de Texto Text Mining LDA Topic modeling Twitter Temporal analysis Sentiment analysis Modelado de temas Análisis temporal Análisis de los sentimientos
title_short	Monitoring the Quality and Perception of Service in Colombian Public Service Companies with Twitter and Descriptive Temporal Analysis
title_full	Monitoring the Quality and Perception of Service in Colombian Public Service Companies with Twitter and Descriptive Temporal Analysis
title_fullStr	Monitoring the Quality and Perception of Service in Colombian Public Service Companies with Twitter and Descriptive Temporal Analysis
title_full_unstemmed	Monitoring the Quality and Perception of Service in Colombian Public Service Companies with Twitter and Descriptive Temporal Analysis
title_sort	Monitoring the Quality and Perception of Service in Colombian Public Service Companies with Twitter and Descriptive Temporal Analysis
dc.creator.fl_str_mv	Conti, Dante Gomez, Carlos Eduardo Jaramillo, Juan Guillermo Ospina, Victoria Eugenia
dc.contributor.author.none.fl_str_mv	Conti, Dante Gomez, Carlos Eduardo Jaramillo, Juan Guillermo Ospina, Victoria Eugenia
dc.contributor.researchgroup.spa.fl_str_mv	CTG - Informática
dc.subject.armarc.none.fl_str_mv	Servicios públicos - Bogotá (Colombia) Public utilities - Bogotá (Colombia) Análisis de la información Information analysis Minería de datos - Bogotá (Colombia) Data mining - Bogotá (Colombia) Extracción de opinión Opinion extraction Minería de Texto Text Mining
topic	Servicios públicos - Bogotá (Colombia) Public utilities - Bogotá (Colombia) Análisis de la información Information analysis Minería de datos - Bogotá (Colombia) Data mining - Bogotá (Colombia) Extracción de opinión Opinion extraction Minería de Texto Text Mining LDA Topic modeling Twitter Temporal analysis Sentiment analysis Modelado de temas Análisis temporal Análisis de los sentimientos
dc.subject.proposal.eng.fl_str_mv	LDA Topic modeling Twitter Temporal analysis Sentiment analysis
dc.subject.proposal.spa.fl_str_mv	Modelado de temas Análisis temporal Análisis de los sentimientos
description	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.
publishDate	2023
dc.date.issued.none.fl_str_mv	2023
dc.date.accessioned.none.fl_str_mv	2024-07-11T19:45:31Z
dc.date.available.none.fl_str_mv	2024-07-11T19:45:31Z
dc.type.spa.fl_str_mv	Artículo de revista
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dc.language.iso.spa.fl_str_mv	eng
language	eng
dc.relation.citationedition.spa.fl_str_mv	Vol. 13 No. 18 2023
dc.relation.citationendpage.spa.fl_str_mv	25
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dc.relation.ispartofjournal.eng.fl_str_mv	Applied Sciences Basel
dc.relation.references.spa.fl_str_mv	Enel. Enel Colombia HomePage. Available online: https://www.enel.com.co/es/prensa/news/d202203-inicio-enel-colombia.html (accessed on 1 March 2022) Vanti. Vanti Home Page. Available online: https://www.grupovanti.com/wp-content/uploads/2021/05/Informe-deSostenibilidad-Vanti.pdf (accessed on 18 September 2021) Empresa de Acueducto y Alcantarillado de Bogotá. Acueducto Bogotá Home Page. Available online: https://www. acueducto.com.co/wps/portal/EAB2/Home/la-empresa/informacion-general/!ut/p/z0/04_Sj9CPykssy0xPLMnMz0 vMAfIjo8zizQKdDQwtDIz8DEyMnA0CgwOcgvxDnQ19jMz0C7IdFQFA0Q31/ (accessed on 16 September 2021) Super Intendencia de Servicios Públicos Domiciliarios. Super Intendencia de Servicios Públicos Home Page. Available online: https://www.superservicios.gov.co/Sala-de-prensa/noticias/en-2020-superservicios-recibio-mas-de-260-mil-tramitesy-solicitudes-de-usuarios-de-los-servicios-publicos-domiciliarios (accessed on 16 August 2021) Enel. Enel Colombia Home Page. Available online: https://www.enel.com.co/content/dam/enel-co/espa%C3%B1ol/sobre_ enel/informes_sostenibiidad/2020/informe-de-sostenibilidad.pdf (accessed on 18 September 2021) Songpan, W. The Analysis and Prediction of Customer Review Rating Using Opinion Mining. In Proceedings of the 7th IEEE International Conference on Software Engineering Research, Management and Applications (SERA), London, UK, 7–9 June 2017; pp. 71–77. [CrossRef] Zhan, Y.; Han, R.; Tse, M.; Helmi Ali, M.; Hu, J. A social media analytic framework for improving operations and service management: A Study Of The Retail Pharmacy Industry. Technol. Forecast. Soc. Change 2021, 163, 11–14. [CrossRef] Bello-Orgaz, G.; Menéndez, H.; Okazaki, S.; Camacho, D. Combining social-based data mining techniques to extract collective trends from Twitter. Malays. J. Comput. Sci. 2014, 27, 95–111. Ngaboyamahina, M.; Sun, Y. The Impact of Sentiment Analysis on social media to Assess Customer Satisfaction: Case of Rwanda. In Proceedings of the International Conference on Big Data Analytics. In Proceedings of the IEEE 4th International Conference on Big Data Analytics (ICBDA), Suzhou, China, 15–18 March 2019; pp. 356–359. Kouloumpis, E.; Wilson, T.; Moore, J. Twitter Sentiment Analysis: The Good the Bad, and the OMG! In Proceedings of the International AAAI Conference on Web and Social Media, Barcelona, Spain, 17–21 July 2011; pp. 538–541. Avila Rodriguez, M.P. Análisis de Tweets y su Influencia en los Seguros de Vida en el Ámbito Colombiano. Master’s Thesis, Universitat Politècnica de València, Valencia, Spain, 2020. Innovare Pesquisa CIER. COCIER Juntos Progresamos. Available online: https://www.cocier.org/index.php/es/ (accessed on 25 October 2021). Chamlertwat, W.; Bhattarakosol, P.; Rungkasiri, T.; Haruechaiyasak, C. Discovering Consumer Insight from Twitter via Sentiment Analysis. J. Univers. Comput. Sci. 2018, 18, 973–992. [CrossRef] Ogudo, K.; Dahj Muwawa Jean, N. Sentiment Analysis Application and Natural Language Processing for Mobile Network Operators’ Support on social media. In Proceedings of the International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD), Winterton, South Africa, 5–6 August 2019; pp. 1–10. Ranjan, S.; Sood, S.; Verma, V. Twitter Sentiment Analysis of Real-time Customer Experience Feedback for Predicting Growth of Indian Telecom Companies. In Proceedings of the 4th International Conference on Computing Sciences (ICCS), Jalandhar, India, 30–31 August 2018; pp. 166–174. [CrossRef] Sari, E.Y.; Wierfi, A.D.; Setyanto, A. Sentiment Analysis of Customer Satisfaction on Transportation Network Company Using Naive Bayes Classifier. In Proceedings of the International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM), Surabaya, Indonesia, 19–20 November 2019; pp. 1–6. Ba, Y.; Lee, H. Sentiment Analysis of Twitter Audiences: Measuring the Positive or Negative Influence of Popular Twitterers. J. Am. Soc. Inf. Sci. Technol. 2012, 63, 2522–2535. [CrossRef] Kuo,W.K.; Riantama, D.; Chen, L.S. Using a Text Mining Approach to Hear Voices of Customers from social media toward the Fast-Food Restaurant Industry. Sustainability 2021, 13, 268. [CrossRef] Fayyad, U.; Piatetsky-Shapiro, G.; Smyth, P. The KDD Process for Extracting Useful Knowledge from Volumes of Data. Commun. ACM1996, 39, 27–34. [CrossRef] Valcárcel Asencios, V. Data mining y el descubrimiento del conocimiento. Ind. Data 2004, 7, 83–86. [CrossRef] RCoreTeam. RHomePage. Available online: https://www.R-project.org/ (accessed on 1 February 2022) Microsoft Corporation. Microsoft Home Page. Available online: https://powerbi.microsoft.com/es-es/ (accessed on 12 October 2022)
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spelling	Conti, Dante144d777fe8ecfb826f48b651faed2493Gomez, Carlos Eduardo4a0b5320a0b454f67df46ee5f717aaccJaramillo, Juan Guillermofd82276d581f10eb7dad12d8e0117c83Ospina, Victoria Eugenia63245421f310579cfd351fedd6db831aCTG - Informática2024-07-11T19:45:31Z2024-07-11T19:45:31Z20232076-3417https://repositorio.escuelaing.edu.co/handle/001/31582076-3417Universidad Escuela Colombiana de Ingeniería Julio GaravitoRepositorio Digitalhttps://repositorio.escuelaing.edu.co/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.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.25 páginasapplication/pdfengMDPI (Multidisciplinary Digital Publishing Institute)Basel (Suiza)https://www.mdpi.com/2076-3417/13/6Monitoring the Quality and Perception of Service in Colombian Public Service Companies with Twitter and Descriptive Temporal AnalysisArtículo de revistainfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/coar/version/c_970fb48d4fbd8a85Vol. 13 No. 18 20232518113Applied Sciences BaselEnel. Enel Colombia HomePage. Available online: https://www.enel.com.co/es/prensa/news/d202203-inicio-enel-colombia.html (accessed on 1 March 2022)Vanti. Vanti Home Page. Available online: https://www.grupovanti.com/wp-content/uploads/2021/05/Informe-deSostenibilidad-Vanti.pdf (accessed on 18 September 2021)Empresa de Acueducto y Alcantarillado de Bogotá. Acueducto Bogotá Home Page. Available online: https://www. acueducto.com.co/wps/portal/EAB2/Home/la-empresa/informacion-general/!ut/p/z0/04_Sj9CPykssy0xPLMnMz0 vMAfIjo8zizQKdDQwtDIz8DEyMnA0CgwOcgvxDnQ19jMz0C7IdFQFA0Q31/ (accessed on 16 September 2021)Super Intendencia de Servicios Públicos Domiciliarios. Super Intendencia de Servicios Públicos Home Page. Available online: https://www.superservicios.gov.co/Sala-de-prensa/noticias/en-2020-superservicios-recibio-mas-de-260-mil-tramitesy-solicitudes-de-usuarios-de-los-servicios-publicos-domiciliarios (accessed on 16 August 2021)Enel. Enel Colombia Home Page. Available online: https://www.enel.com.co/content/dam/enel-co/espa%C3%B1ol/sobre_ enel/informes_sostenibiidad/2020/informe-de-sostenibilidad.pdf (accessed on 18 September 2021)Songpan, W. The Analysis and Prediction of Customer Review Rating Using Opinion Mining. In Proceedings of the 7th IEEE International Conference on Software Engineering Research, Management and Applications (SERA), London, UK, 7–9 June 2017; pp. 71–77. [CrossRef]Zhan, Y.; Han, R.; Tse, M.; Helmi Ali, M.; Hu, J. A social media analytic framework for improving operations and service management: A Study Of The Retail Pharmacy Industry. Technol. Forecast. Soc. Change 2021, 163, 11–14. [CrossRef]Bello-Orgaz, G.; Menéndez, H.; Okazaki, S.; Camacho, D. Combining social-based data mining techniques to extract collective trends from Twitter. Malays. J. Comput. Sci. 2014, 27, 95–111.Ngaboyamahina, M.; Sun, Y. The Impact of Sentiment Analysis on social media to Assess Customer Satisfaction: Case of Rwanda. In Proceedings of the International Conference on Big Data Analytics. In Proceedings of the IEEE 4th International Conference on Big Data Analytics (ICBDA), Suzhou, China, 15–18 March 2019; pp. 356–359.Kouloumpis, E.; Wilson, T.; Moore, J. Twitter Sentiment Analysis: The Good the Bad, and the OMG! In Proceedings of the International AAAI Conference on Web and Social Media, Barcelona, Spain, 17–21 July 2011; pp. 538–541.Avila Rodriguez, M.P. Análisis de Tweets y su Influencia en los Seguros de Vida en el Ámbito Colombiano. Master’s Thesis, Universitat Politècnica de València, Valencia, Spain, 2020.Innovare Pesquisa CIER. COCIER Juntos Progresamos. Available online: https://www.cocier.org/index.php/es/ (accessed on 25 October 2021).Chamlertwat, W.; Bhattarakosol, P.; Rungkasiri, T.; Haruechaiyasak, C. Discovering Consumer Insight from Twitter via Sentiment Analysis. J. Univers. Comput. Sci. 2018, 18, 973–992. [CrossRef]Ogudo, K.; Dahj Muwawa Jean, N. Sentiment Analysis Application and Natural Language Processing for Mobile Network Operators’ Support on social media. In Proceedings of the International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD), Winterton, South Africa, 5–6 August 2019; pp. 1–10.Ranjan, S.; Sood, S.; Verma, V. Twitter Sentiment Analysis of Real-time Customer Experience Feedback for Predicting Growth of Indian Telecom Companies. In Proceedings of the 4th International Conference on Computing Sciences (ICCS), Jalandhar, India, 30–31 August 2018; pp. 166–174. [CrossRef]Sari, E.Y.; Wierfi, A.D.; Setyanto, A. Sentiment Analysis of Customer Satisfaction on Transportation Network Company Using Naive Bayes Classifier. In Proceedings of the International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM), Surabaya, Indonesia, 19–20 November 2019; pp. 1–6.Ba, Y.; Lee, H. Sentiment Analysis of Twitter Audiences: Measuring the Positive or Negative Influence of Popular Twitterers. J. Am. Soc. Inf. Sci. Technol. 2012, 63, 2522–2535. [CrossRef]Kuo,W.K.; Riantama, D.; Chen, L.S. Using a Text Mining Approach to Hear Voices of Customers from social media toward the Fast-Food Restaurant Industry. Sustainability 2021, 13, 268. [CrossRef]Fayyad, U.; Piatetsky-Shapiro, G.; Smyth, P. The KDD Process for Extracting Useful Knowledge from Volumes of Data. Commun. ACM1996, 39, 27–34. [CrossRef]Valcárcel Asencios, V. Data mining y el descubrimiento del conocimiento. Ind. Data 2004, 7, 83–86. [CrossRef]RCoreTeam. RHomePage. Available online: https://www.R-project.org/ (accessed on 1 February 2022)Microsoft Corporation. Microsoft Home Page. Available online: https://powerbi.microsoft.com/es-es/ (accessed on 12 October 2022)info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Servicios públicos - Bogotá (Colombia)Public utilities - Bogotá (Colombia)Análisis de la informaciónInformation analysisMinería de datos - Bogotá (Colombia)Data mining - Bogotá (Colombia)Extracción de opiniónOpinion extractionMinería de TextoText MiningLDATopic modelingTwitterTemporal analysisSentiment analysisModelado de temasAnálisis temporalAnálisis de los sentimientosTEXTMonitoring the quality and perception of service in Colombian public service companies.pdf.txtMonitoring the quality and perception of service in Colombian public service companies.pdf.txtExtracted texttext/plain92568https://repositorio.escuelaing.edu.co/bitstream/001/3158/4/Monitoring%20the%20quality%20and%20perception%20of%20service%20in%20Colombian%20public%20service%20companies.pdf.txt1a4b40f6b343cbee3485330590ebf070MD54open accessTHUMBNAILPortada Monitoring the quality and perception of service in Colombian public service companies.PNGPortada Monitoring the quality and perception of service in Colombian public service companies.PNGimage/png303127https://repositorio.escuelaing.edu.co/bitstream/001/3158/3/Portada%20Monitoring%20the%20quality%20and%20perception%20of%20service%20in%20Colombian%20public%20service%20companies.PNG3b99bdf436d8478dce6f93438b45e099MD53open accessMonitoring the quality and perception of service in Colombian public service companies.pdf.jpgMonitoring the quality and perception of service in Colombian public service companies.pdf.jpgGenerated Thumbnailimage/jpeg16078https://repositorio.escuelaing.edu.co/bitstream/001/3158/5/Monitoring%20the%20quality%20and%20perception%20of%20service%20in%20Colombian%20public%20service%20companies.pdf.jpg3ab01431084a265ed45280cdf34a6e7cMD55open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-81881https://repositorio.escuelaing.edu.co/bitstream/001/3158/2/license.txt5a7ca94c2e5326ee169f979d71d0f06eMD52open accessORIGINALMonitoring the quality and perception of service in Colombian public service companies.pdfMonitoring the quality and perception of service in Colombian public service companies.pdfapplication/pdf11911174https://repositorio.escuelaing.edu.co/bitstream/001/3158/1/Monitoring%20the%20quality%20and%20perception%20of%20service%20in%20Colombian%20public%20service%20companies.pdffc49d3c1cfada557b73aed6f50fd7bf8MD51metadata only access001/3158oai:repositorio.escuelaing.edu.co:001/31582024-08-06 16:12:35.614metadata only accessRepositorio Escuela Colombiana de Ingeniería Julio Garavitorepositorio.eci@escuelaing.edu.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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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