Prototipo de un clasificador de sentimientos para chats de atención al cliente en canales digitales del sector salud

ilustraciones, diagramas

Autores:: Ardila Franco, César Augusto

Tipo de recurso:

Fecha de publicación:: 2023

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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dc.title.spa.fl_str_mv	Prototipo de un clasificador de sentimientos para chats de atención al cliente en canales digitales del sector salud
dc.title.translated.eng.fl_str_mv	Prototype of a sentiment classifier for customer service chats in digital channels of the health sector
title	Prototipo de un clasificador de sentimientos para chats de atención al cliente en canales digitales del sector salud
spellingShingle	Prototipo de un clasificador de sentimientos para chats de atención al cliente en canales digitales del sector salud Redes sociales Social networks Analítica de texto Modelos de clasificación textual Análisis de polaridad Canales digitales Sector salud Externalización de Procesos de Negocio Bussiness Processing Outsourcing (BPO) Text analytics Text classification models Polarity analysis Digital channels Health sector
title_short	Prototipo de un clasificador de sentimientos para chats de atención al cliente en canales digitales del sector salud
title_full	Prototipo de un clasificador de sentimientos para chats de atención al cliente en canales digitales del sector salud
title_fullStr	Prototipo de un clasificador de sentimientos para chats de atención al cliente en canales digitales del sector salud
title_full_unstemmed	Prototipo de un clasificador de sentimientos para chats de atención al cliente en canales digitales del sector salud
title_sort	Prototipo de un clasificador de sentimientos para chats de atención al cliente en canales digitales del sector salud
dc.creator.fl_str_mv	Ardila Franco, César Augusto
dc.contributor.advisor.none.fl_str_mv	Velásquez Henao, Juan David
dc.contributor.author.none.fl_str_mv	Ardila Franco, César Augusto
dc.subject.lemb.spa.fl_str_mv	Redes sociales
topic	Redes sociales Social networks Analítica de texto Modelos de clasificación textual Análisis de polaridad Canales digitales Sector salud Externalización de Procesos de Negocio Bussiness Processing Outsourcing (BPO) Text analytics Text classification models Polarity analysis Digital channels Health sector
dc.subject.lemb.eng.fl_str_mv	Social networks
dc.subject.proposal.spa.fl_str_mv	Analítica de texto Modelos de clasificación textual Análisis de polaridad Canales digitales Sector salud Externalización de Procesos de Negocio
dc.subject.proposal.eng.fl_str_mv	Bussiness Processing Outsourcing (BPO) Text analytics Text classification models Polarity analysis Digital channels Health sector
description	ilustraciones, diagramas
publishDate	2023
dc.date.accessioned.none.fl_str_mv	2023-06-09T14:42:59Z
dc.date.available.none.fl_str_mv	2023-06-09T14:42:59Z
dc.date.issued.none.fl_str_mv	2023-01-19
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
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dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/84001 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.indexed.spa.fl_str_mv	RedCol LaReferencia
dc.relation.references.spa.fl_str_mv	F. Reichheld, «The one number you need to grow,» Harvard business review, vol. 81, nº 124, pp. 46-54, 2003. B. Lakdawala, F. Khan, A. Khan, Y. Tomar, R. Gupta y A. Shaikh, «Voice to Text transcription using CMU Sphinx A mobile application for healthcare organization,» de Second International Conference on Inventive Communication and Computational Technologies (ICICCT), 2018. S. Liu, «Bridging Text Visualization and Mining: A Task-Driven Survey,» IEEE Transactions on Visualization and Computer Graphics, vol. 25, nº 7, pp. 2482-2504, 2019. S. Loria, «textblob Documentation,» Release 0.15, vol. 2, 2018. J. M. Pérez, J. C. Giudici y F. Luque, «pysentimiento: A Python Toolkit for Sentiment Analysis and SocialNLP tasks,» 2021. A. Krizhevsky, I. Sutskever y G. E. Hinton, «Imagenet classification with deep convolutional neural networks,» de Neural. Information Processing Systems (NIPS), 2012. K. V. Raju y M. Sridhar, «Sentimental Analysis Inclination, A Review,» de International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC), 2017. M. T. Pilehvar y J. Camacho-Collados, «Embeddings in Natural Language Processing: Theory and Advances in Vector Representations of Meaning, Morgan & Claypool,» 2020. A. Vaswani, «Attention is all you need,» Advances in Neural Information Processing Systems, pp. 5998-6008, 2017. J. Canete, Chaperon, Gabriel, Fuentes, Rodrigo, J.-H. Ho, Kang, Hojin y J. Pérez, «Spanish pre-trained bert model and evaluation data,» Pml4dc at iclr, 2020. T. Young, D. Hazarika, S. Poria y E. Cambria, «Recent Trends in Deep Learning Based Natural Language Processing,» IEEE Computational Intelligence Magazine, vol. 13, nº 3, pp. 55-75, 2018. S. Yang, Z. Ning y Y. Wu, «NLP Based on Twitter Information: A Survey Report,» de 2nd International Conference on Information Technology and Computer Application (ITCA), 2020. S. Z. Mishu y S. M. Rafiuddin, «Performance analysis of supervised machine learning algorithms for text classification,» de 19th International Conference on Computer and Information Technology (ICCIT), 2016. Aggarwal, C. Charu y C. Zhai, «A survey of text classification algorithms,» Springer US, pp. 163-222, 2012. Li, Xiaoli y B. Liu, «Learning to classify texts using positive and unlabeled data,» IJCAI, vol. 3, 2003. Tong, Simon y D. Koller, «Support vector machine active learning with applications to text classification,» Journal of machine learning research 2, pp. 45-66, 2001. Liu y Bing, «Text classification by labeling words,» AAAI, vol. 4, 2004. Schütze y Hinrich, «Introduction to Information Retrieval,» de Proceedings of the international communication of association for computing machinery conference, 2008. Tang y Bo, «A Bayesian classification approach using class-specific features for text categorization,» IEEE Transactions on Knowledge and Data Engineering, vol. 28, nº 6, pp. 1602-1606, 2016. S. J. S. a. G. M. N. Arunachalam, «A survey on text classification techniques for sentiment polarity detection,» Innovations in Power and Advanced Computing Technologies (i-PACT), pp. 1-5, 2017. Z. Li, W. Shang y M. Yan, «News text classification model based on topic model,» 2016. M. I. Khaleel, I. I. Hmeidi y H. M. Najadat, «An Automatic Text Classification System Based on Genetic Algorithm,» 2016. J. Devlin, M. W. Chang, K. Lee y K. Toutanova, «Bert: Pre-training of deep bidirectional transformers for language understanding,» arXiv preprint. M. E. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee y L. Zettlemoyer, «Deep contextualized word representations,» arXiv preprint, 2018. J. Howard y S. Ruder, «Universal language model fine-tuning for text classification,» arXiv preprint, 2018. J. Ara, M. T. Hasan, A. A. Omar y H. Bhuiyan, «Understanding Customer Sentiment: Lexical Analysis of Restaurant Reviews,» de IEEE Region 10 Symposium (TENSYMP), 2020. M. N. a. S. Yi, «The Impact of Sentiment Analysis on Social Media to Assess Customer Satisfaction: Case of Rwanda,» de IEEE 4th International Conference on Big Data Analytics (ICBDA), 2019. S. Lam, C. Chen, K. Kim, G. Wilson, J. H. Crew y M. S. Gerber, «Optimizing Customer-Agent Interactions with Natural Language Processing and Machine Learning,» de Systems and Information Engineering Design Symposium (SIEDS), 2019. C. A. Haryani, A. N. Hidayanto, N. F. A. Budi y Herkules, «Sentiment Analysis of Online Auction Service Quality on Twitter Data: A case of E-Bay,» de 6th International Conference on Cyber and IT Service Management (CITSM), 2018. D. Wu, «A big data analytics framework for forecasting rare customer complaints: A use case of predicting MA members complaints to CMS,» de IEEE International Conference on Big Data (Big Data), 2017. A. I. Pandesenda, R. R. Yana, E. A. Sukma, A. Yahya, P. Widharto y A. N. Hidayanto, «Sentiment Analysis of Service Quality of Online Healthcare Platform Using Fast Large-Margin,» de International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS), 2020. G. Saranya, G. Geetha, C. K, M. K y S. Karpagaselvi, «Sentiment analysis of healthcare Tweets using SVM Classifier,» de International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), 2020. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion y O. o. Grisel, «Scikit-learn: Machine learning in Python,» Journal of Machine Learning Research, vol. 12, pp. 2825-2830, 2011. M. G. Vega, M. C. Díaz y others, Overview of TASS 2020: Introducing Emotion Detection, IberLEF@SEPLN, 2020. M. U. SALUR y İ. AYDIN, «The Impact of Preprocessing on Classification Performance in Convolutional Neural Networks for Turkish Text,» de International Conference on Artificial Intelligence and Data Processing (IDAP), 2018. P. Chandrasekar y K. Qian, «The Impact of Data Preprocessing on the Performance of a Naive Bayes Classifier,» de IEEE 40th Annual Computer Software and Applications Conference (COMPSAC), 2016. A. Kurbatow, «The research of text preprocessing effect on text documents classification efficiency,» de International Conference "Stability and Control Processes" in Memory of V.I. Zubov (SCP), 2015. A. K. B y M. M. Kodabagi, «Efficient Data Preprocessing approach for Imbalanced Data in Email Classification System,» de International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), 2020.
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rights_invalid_str_mv	Atribución-NoComercial-SinDerivadas 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ http://purl.org/coar/access_right/c_abf2
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dc.format.extent.spa.fl_str_mv	66 páginas
dc.format.mimetype.spa.fl_str_mv	application/pdf
dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Medellín - Minas - Maestría en Ingeniería - Analítica
dc.publisher.faculty.spa.fl_str_mv	Facultad de Minas
dc.publisher.place.spa.fl_str_mv	Medellín, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Medellín
institution	Universidad Nacional de Colombia
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spelling	Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Velásquez Henao, Juan David7b16d4a5377f0f1b1f90d3c8c6fd9f8bArdila Franco, César Augustoaac24e04945dc72eb57731c88bcf9eed2023-06-09T14:42:59Z2023-06-09T14:42:59Z2023-01-19https://repositorio.unal.edu.co/handle/unal/84001Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramasEl presente trabajo evalúa el modelo Pysentimiento para extraer la polaridad (negativo, neutro o positivo) de mensajes que pertenecen a un canal digital del sector salud y propone un esquema compuesto por tres subsistemas para incrementar el rendimiento del clasificador de emociones: 1) aplicar el preprocesamiento correcto los mensajes del corpus; 2) generar una tabla de expresiones comunes que facilite la clasificación de mensajes con polaridad neutra (NEU) y 3) construir un sistema de alerta que permita a los analistas identificar cuándo la predicción de un sentimiento puede considerarse ambigua. El nuevo esquema, además de presentar un incremento en rendimiento, permite también gestionar la información con el objetivo de caracterizar los mensajes de canales digitales del sector salud, y por ende, facilitar la implementación de nuevos clasificadores de emociones. (Texto tomado de la fuente)This paper evaluates a model called Pysentimiento to extract the polarity (negative, neutral, or positive) of messages that belong to a digital channel in the health sector. It also proposes a scheme made up of three subsystems to increase the performance of the sentiment classifier: 1) apply the correct preprocessing to the corpus messages; 2) generate a table of common expressions that facilitates the classification of messages with neutral polarity (NEU) and 3) build an alert system that allows analysts to identify when the prediction of a sentiment can be considered ambiguous. The new scheme, in addition to presenting an increase in performance, also makes it possible to manage the information in order to characterize the messages from digital channels in the health sector, and therefore, facilitate the implementation of new emotion classifiers.MaestríaMagíster en Ingeniería - AnalíticaAnalítica PredictivaÁrea Curricular de Ingeniería de Sistemas e Informática66 páginasapplication/pdfspaUniversidad Nacional de ColombiaMedellín - Minas - Maestría en Ingeniería - AnalíticaFacultad de MinasMedellín, ColombiaUniversidad Nacional de Colombia - Sede MedellínPrototipo de un clasificador de sentimientos para chats de atención al cliente en canales digitales del sector saludPrototype of a sentiment classifier for customer service chats in digital channels of the health sectorTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMRedColLaReferenciaF. Reichheld, «The one number you need to grow,» Harvard business review, vol. 81, nº 124, pp. 46-54, 2003.B. Lakdawala, F. Khan, A. Khan, Y. Tomar, R. Gupta y A. Shaikh, «Voice to Text transcription using CMU Sphinx A mobile application for healthcare organization,» de Second International Conference on Inventive Communication and Computational Technologies (ICICCT), 2018.S. Liu, «Bridging Text Visualization and Mining: A Task-Driven Survey,» IEEE Transactions on Visualization and Computer Graphics, vol. 25, nº 7, pp. 2482-2504, 2019.S. Loria, «textblob Documentation,» Release 0.15, vol. 2, 2018.J. M. Pérez, J. C. Giudici y F. Luque, «pysentimiento: A Python Toolkit for Sentiment Analysis and SocialNLP tasks,» 2021.A. Krizhevsky, I. Sutskever y G. E. Hinton, «Imagenet classification with deep convolutional neural networks,» de Neural. Information Processing Systems (NIPS), 2012.K. V. Raju y M. Sridhar, «Sentimental Analysis Inclination, A Review,» de International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC), 2017.M. T. Pilehvar y J. Camacho-Collados, «Embeddings in Natural Language Processing: Theory and Advances in Vector Representations of Meaning, Morgan & Claypool,» 2020.A. Vaswani, «Attention is all you need,» Advances in Neural Information Processing Systems, pp. 5998-6008, 2017.J. Canete, Chaperon, Gabriel, Fuentes, Rodrigo, J.-H. Ho, Kang, Hojin y J. Pérez, «Spanish pre-trained bert model and evaluation data,» Pml4dc at iclr, 2020.T. Young, D. Hazarika, S. Poria y E. Cambria, «Recent Trends in Deep Learning Based Natural Language Processing,» IEEE Computational Intelligence Magazine, vol. 13, nº 3, pp. 55-75, 2018.S. Yang, Z. Ning y Y. Wu, «NLP Based on Twitter Information: A Survey Report,» de 2nd International Conference on Information Technology and Computer Application (ITCA), 2020.S. Z. Mishu y S. M. Rafiuddin, «Performance analysis of supervised machine learning algorithms for text classification,» de 19th International Conference on Computer and Information Technology (ICCIT), 2016.Aggarwal, C. Charu y C. Zhai, «A survey of text classification algorithms,» Springer US, pp. 163-222, 2012.Li, Xiaoli y B. Liu, «Learning to classify texts using positive and unlabeled data,» IJCAI, vol. 3, 2003.Tong, Simon y D. Koller, «Support vector machine active learning with applications to text classification,» Journal of machine learning research 2, pp. 45-66, 2001.Liu y Bing, «Text classification by labeling words,» AAAI, vol. 4, 2004.Schütze y Hinrich, «Introduction to Information Retrieval,» de Proceedings of the international communication of association for computing machinery conference, 2008.Tang y Bo, «A Bayesian classification approach using class-specific features for text categorization,» IEEE Transactions on Knowledge and Data Engineering, vol. 28, nº 6, pp. 1602-1606, 2016.S. J. S. a. G. M. N. Arunachalam, «A survey on text classification techniques for sentiment polarity detection,» Innovations in Power and Advanced Computing Technologies (i-PACT), pp. 1-5, 2017.Z. Li, W. Shang y M. Yan, «News text classification model based on topic model,» 2016.M. I. Khaleel, I. I. Hmeidi y H. M. Najadat, «An Automatic Text Classification System Based on Genetic Algorithm,» 2016.J. Devlin, M. W. Chang, K. Lee y K. Toutanova, «Bert: Pre-training of deep bidirectional transformers for language understanding,» arXiv preprint.M. E. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee y L. Zettlemoyer, «Deep contextualized word representations,» arXiv preprint, 2018.J. Howard y S. Ruder, «Universal language model fine-tuning for text classification,» arXiv preprint, 2018.J. Ara, M. T. Hasan, A. A. Omar y H. Bhuiyan, «Understanding Customer Sentiment: Lexical Analysis of Restaurant Reviews,» de IEEE Region 10 Symposium (TENSYMP), 2020.M. N. a. S. Yi, «The Impact of Sentiment Analysis on Social Media to Assess Customer Satisfaction: Case of Rwanda,» de IEEE 4th International Conference on Big Data Analytics (ICBDA), 2019.S. Lam, C. Chen, K. Kim, G. Wilson, J. H. Crew y M. S. Gerber, «Optimizing Customer-Agent Interactions with Natural Language Processing and Machine Learning,» de Systems and Information Engineering Design Symposium (SIEDS), 2019.C. A. Haryani, A. N. Hidayanto, N. F. A. Budi y Herkules, «Sentiment Analysis of Online Auction Service Quality on Twitter Data: A case of E-Bay,» de 6th International Conference on Cyber and IT Service Management (CITSM), 2018.D. Wu, «A big data analytics framework for forecasting rare customer complaints: A use case of predicting MA members complaints to CMS,» de IEEE International Conference on Big Data (Big Data), 2017.A. I. Pandesenda, R. R. Yana, E. A. Sukma, A. Yahya, P. Widharto y A. N. Hidayanto, «Sentiment Analysis of Service Quality of Online Healthcare Platform Using Fast Large-Margin,» de International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS), 2020.G. Saranya, G. Geetha, C. K, M. K y S. Karpagaselvi, «Sentiment analysis of healthcare Tweets using SVM Classifier,» de International Conference on Power, Energy, Control and Transmission Systems (ICPECTS), 2020.F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion y O. o. Grisel, «Scikit-learn: Machine learning in Python,» Journal of Machine Learning Research, vol. 12, pp. 2825-2830, 2011.M. G. Vega, M. C. Díaz y others, Overview of TASS 2020: Introducing Emotion Detection, IberLEF@SEPLN, 2020.M. U. SALUR y İ. AYDIN, «The Impact of Preprocessing on Classification Performance in Convolutional Neural Networks for Turkish Text,» de International Conference on Artificial Intelligence and Data Processing (IDAP), 2018.P. Chandrasekar y K. Qian, «The Impact of Data Preprocessing on the Performance of a Naive Bayes Classifier,» de IEEE 40th Annual Computer Software and Applications Conference (COMPSAC), 2016.A. Kurbatow, «The research of text preprocessing effect on text documents classification efficiency,» de International Conference "Stability and Control Processes" in Memory of V.I. Zubov (SCP), 2015.A. K. B y M. M. Kodabagi, «Efficient Data Preprocessing approach for Imbalanced Data in Email Classification System,» de International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), 2020.Redes socialesSocial networksAnalítica de textoModelos de clasificación textualAnálisis de polaridadCanales digitalesSector saludExternalización de Procesos de NegocioBussiness Processing Outsourcing (BPO)Text analyticsText classification modelsPolarity analysisDigital channelsHealth sectorInvestigadoresLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/84001/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1144098500.2023.pdf1144098500.2023.pdfTesis de Maestría en Ingeniería - Analíticaapplication/pdf1294524https://repositorio.unal.edu.co/bitstream/unal/84001/2/1144098500.2023.pdf96b6fba0134d190399433c384295546cMD52THUMBNAIL1144098500.2023.pdf.jpg1144098500.2023.pdf.jpgGenerated 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