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Machine Learning Algorithms for Detection and Classifications of Emotions in Contact Center Applications
Over the past few years, virtual assistant solutions used in Contact Center systems are gaining popularity. One of the main tasks of the virtual assistant is to recognize the intentions of the customer. It is important to note that quite often the actual intention expressed in a conversation is also...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9321989/ https://www.ncbi.nlm.nih.gov/pubmed/35890994 http://dx.doi.org/10.3390/s22145311 |
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author | Płaza, Mirosław Trusz, Sławomir Kęczkowska, Justyna Boksa, Ewa Sadowski, Sebastian Koruba, Zbigniew |
author_facet | Płaza, Mirosław Trusz, Sławomir Kęczkowska, Justyna Boksa, Ewa Sadowski, Sebastian Koruba, Zbigniew |
author_sort | Płaza, Mirosław |
collection | PubMed |
description | Over the past few years, virtual assistant solutions used in Contact Center systems are gaining popularity. One of the main tasks of the virtual assistant is to recognize the intentions of the customer. It is important to note that quite often the actual intention expressed in a conversation is also directly influenced by the emotions that accompany that conversation. Unfortunately, scientific literature has not identified what specific types of emotions in Contact Center applications are relevant to the activities they perform. Therefore, the main objective of this work was to develop an Emotion Classification for Machine Detection of Affect-Tinged Conversational Contents dedicated directly to the Contact Center industry. In the conducted study, Contact Center voice and text channels were considered, taking into account the following families of emotions: anger, fear, happiness, sadness vs. affective neutrality of the statements. The obtained results confirmed the usefulness of the proposed classification—for the voice channel, the highest efficiency was obtained using the Convolutional Neural Network (accuracy, 67.5%; precision, 80.3; F1-Score, 74.5%), while for the text channel, the Support Vector Machine algorithm proved to be the most efficient (accuracy, 65.9%; precision, 58.5; F1-Score, 61.7%). |
format | Online Article Text |
id | pubmed-9321989 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93219892022-07-27 Machine Learning Algorithms for Detection and Classifications of Emotions in Contact Center Applications Płaza, Mirosław Trusz, Sławomir Kęczkowska, Justyna Boksa, Ewa Sadowski, Sebastian Koruba, Zbigniew Sensors (Basel) Article Over the past few years, virtual assistant solutions used in Contact Center systems are gaining popularity. One of the main tasks of the virtual assistant is to recognize the intentions of the customer. It is important to note that quite often the actual intention expressed in a conversation is also directly influenced by the emotions that accompany that conversation. Unfortunately, scientific literature has not identified what specific types of emotions in Contact Center applications are relevant to the activities they perform. Therefore, the main objective of this work was to develop an Emotion Classification for Machine Detection of Affect-Tinged Conversational Contents dedicated directly to the Contact Center industry. In the conducted study, Contact Center voice and text channels were considered, taking into account the following families of emotions: anger, fear, happiness, sadness vs. affective neutrality of the statements. The obtained results confirmed the usefulness of the proposed classification—for the voice channel, the highest efficiency was obtained using the Convolutional Neural Network (accuracy, 67.5%; precision, 80.3; F1-Score, 74.5%), while for the text channel, the Support Vector Machine algorithm proved to be the most efficient (accuracy, 65.9%; precision, 58.5; F1-Score, 61.7%). MDPI 2022-07-15 /pmc/articles/PMC9321989/ /pubmed/35890994 http://dx.doi.org/10.3390/s22145311 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Płaza, Mirosław Trusz, Sławomir Kęczkowska, Justyna Boksa, Ewa Sadowski, Sebastian Koruba, Zbigniew Machine Learning Algorithms for Detection and Classifications of Emotions in Contact Center Applications |
title | Machine Learning Algorithms for Detection and Classifications of Emotions in Contact Center Applications |
title_full | Machine Learning Algorithms for Detection and Classifications of Emotions in Contact Center Applications |
title_fullStr | Machine Learning Algorithms for Detection and Classifications of Emotions in Contact Center Applications |
title_full_unstemmed | Machine Learning Algorithms for Detection and Classifications of Emotions in Contact Center Applications |
title_short | Machine Learning Algorithms for Detection and Classifications of Emotions in Contact Center Applications |
title_sort | machine learning algorithms for detection and classifications of emotions in contact center applications |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9321989/ https://www.ncbi.nlm.nih.gov/pubmed/35890994 http://dx.doi.org/10.3390/s22145311 |
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