dc.contributor | Montoya Múnera, Edwin Nelson | |
dc.creator | Flórez Salazar, Luz Stella | |
dc.date.accessioned | 2023-05-08T15:57:53Z | |
dc.date.accessioned | 2023-08-28T14:12:36Z | |
dc.date.available | 2023-05-08T15:57:53Z | |
dc.date.available | 2023-08-28T14:12:36Z | |
dc.date.created | 2023-05-08T15:57:53Z | |
dc.date.issued | 2023 | |
dc.identifier | http://hdl.handle.net/10784/32428 | |
dc.identifier | 006.3 F634 | |
dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/8441645 | |
dc.description.abstract | A 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.language | spa | |
dc.publisher | Universidad EAFIT | |
dc.publisher | Maestría en Ciencias de los Datos y Analítica | |
dc.publisher | Escuela de Administración | |
dc.publisher | Medellín | |
dc.rights | info:eu-repo/semantics/openAccess | |
dc.rights | Acceso abierto | |
dc.rights | Todos los derechos reservados | |
dc.subject | Análisis de sentimientos | |
dc.subject | Chatbot | |
dc.subject | Escalamiento a asesor humano | |
dc.subject | Inteligencia artificial | |
dc.subject | Procesamiento de lenguaje natural | |
dc.title | Análisis 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.type | masterThesis | |
dc.type | info:eu-repo/semantics/masterThesis | |