dc.contributorMontoya Múnera, Edwin Nelson
dc.creatorFlórez Salazar, Luz Stella
dc.date.accessioned2023-05-08T15:57:53Z
dc.date.accessioned2023-08-28T14:12:36Z
dc.date.available2023-05-08T15:57:53Z
dc.date.available2023-08-28T14:12:36Z
dc.date.created2023-05-08T15:57:53Z
dc.date.issued2023
dc.identifierhttp://hdl.handle.net/10784/32428
dc.identifier006.3 F634
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/8441645
dc.description.abstractA chatbot is a program created with artificial intelligence. In the context of customer service, can establish conversations with customers and they are trained to resolve their queries, problems and complaints. A chatbot’s skill to identify when a customer is not meeting their request represents a challenge for companies that currently use this technology. One of the strategies to avoid quitting the conversation for this reason, is to escalate or transfer the conversation to a human agent. Therefore, it is essential to detect when it is time to carry out this escalation. This project evaluates different Natural Language Processing (NLP) techniques, rule-based labeling algorithms, classical supervised machine learning models and a simple neural network for classification, applied to interactions between a customer service chatbot and a user, in order to find a mechanism for automatic labeling of the data and to build a model that can be used to make the decision on whether the customer should continue interacting with the chatbot or if he should be transferred to a conversation with a human agent. The labeling mechanism could also be used to classify historical data, to later train a model. Different models and techniques are evaluated and those with the best performance in detecting the conversations that should escalate to a human agent are presented.
dc.languagespa
dc.publisherUniversidad EAFIT
dc.publisherMaestría en Ciencias de los Datos y Analítica
dc.publisherEscuela de Administración
dc.publisherMedellín
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsAcceso abierto
dc.rightsTodos los derechos reservados
dc.subjectAnálisis de sentimientos
dc.subjectChatbot
dc.subjectEscalamiento a asesor humano
dc.subjectInteligencia artificial
dc.subjectProcesamiento de lenguaje natural
dc.titleAnálisis de la tendencia de la solución de una interacción con un Chatbot de atención al cliente, basado en análisis de sentimiento y otras variables
dc.typemasterThesis
dc.typeinfo:eu-repo/semantics/masterThesis


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