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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/14/5311 |
| _version_ | 1797433096834908160 |
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| author | Mirosław Płaza Sławomir Trusz Justyna Kęczkowska Ewa Boksa Sebastian Sadowski Zbigniew Koruba |
| author_facet | Mirosław Płaza Sławomir Trusz Justyna Kęczkowska Ewa Boksa Sebastian Sadowski Zbigniew Koruba |
| author_sort | Mirosław Płaza |
| collection | DOAJ |
| 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 <i>Emotion Classification for Machine Detection of Affect-Tinged Conversational Contents</i> 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%). |
| first_indexed | 2024-03-09T10:12:16Z |
| format | Article |
| id | doaj.art-2774ae0a473d4d2aa1ab28cf35897bfc |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T10:12:16Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-2774ae0a473d4d2aa1ab28cf35897bfc2023-12-01T22:40:30ZengMDPI AGSensors1424-82202022-07-012214531110.3390/s22145311Machine Learning Algorithms for Detection and Classifications of Emotions in Contact Center ApplicationsMirosław Płaza0Sławomir Trusz1Justyna Kęczkowska2Ewa Boksa3Sebastian Sadowski4Zbigniew Koruba5Faculty of Electrical Engineering, Automatic Control and Computer Science, Kielce University of Technology, Al. Tysiąclecia P.P. 7, 25-314 Kielce, PolandInstitute of Educational Sciences, Pedagogical University in Kraków, ul. 4 Ingardena, 30-060 Cracow, PolandFaculty of Electrical Engineering, Automatic Control and Computer Science, Kielce University of Technology, Al. Tysiąclecia P.P. 7, 25-314 Kielce, PolandFaculty of Humanities, Jan Kochanowski University, ul. Żeromskiego 5, 25-369 Kielce, PolandDHL Parcel Poland, ul. Osmańska 2, 02-823 Warszawa, PolandFaculty of Mechatronics and Mechanical Engineering, Kielce University of Technology, Al. Tysiąclecia P.P. 7, 25-314 Kielce, PolandOver 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 <i>Emotion Classification for Machine Detection of Affect-Tinged Conversational Contents</i> 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%).https://www.mdpi.com/1424-8220/22/14/5311call/contact centeremotions recognitionvirtual assistantvoicebotchatbot |
| spellingShingle | Mirosław Płaza Sławomir Trusz Justyna Kęczkowska Ewa Boksa Sebastian Sadowski Zbigniew Koruba Machine Learning Algorithms for Detection and Classifications of Emotions in Contact Center Applications Sensors call/contact center emotions recognition virtual assistant voicebot chatbot |
| 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 | call/contact center emotions recognition virtual assistant voicebot chatbot |
| url | https://www.mdpi.com/1424-8220/22/14/5311 |
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