{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T14:51:11Z","timestamp":1774536671686,"version":"3.50.1"},"reference-count":94,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,15]],"date-time":"2022-07-15T00:00:00Z","timestamp":1657843200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005632","name":"European Union\u2019s Smart Growth Operational Programme","doi-asserted-by":"publisher","award":["POIR.04.01.04-00-0079\/19"],"award-info":[{"award-number":["POIR.04.01.04-00-0079\/19"]}],"id":[{"id":"10.13039\/501100005632","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2014for 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%).<\/jats:p>","DOI":"10.3390\/s22145311","type":"journal-article","created":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T01:53:22Z","timestamp":1658109202000},"page":"5311","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Machine Learning Algorithms for Detection and Classifications of Emotions in Contact Center Applications"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9728-3630","authenticated-orcid":false,"given":"Miros\u0142aw","family":"P\u0142aza","sequence":"first","affiliation":[{"name":"Faculty of Electrical Engineering, Automatic Control and Computer Science, Kielce University of Technology, Al. Tysi\u0105clecia P.P. 7, 25-314 Kielce, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7572-018X","authenticated-orcid":false,"given":"S\u0142awomir","family":"Trusz","sequence":"additional","affiliation":[{"name":"Institute of Educational Sciences, Pedagogical University in Krak\u00f3w, ul. 4 Ingardena, 30-060 Cracow, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2309-3065","authenticated-orcid":false,"given":"Justyna","family":"K\u0119czkowska","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, Automatic Control and Computer Science, Kielce University of Technology, Al. Tysi\u0105clecia P.P. 7, 25-314 Kielce, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3041-8283","authenticated-orcid":false,"given":"Ewa","family":"Boksa","sequence":"additional","affiliation":[{"name":"Faculty of Humanities, Jan Kochanowski University, ul. \u017beromskiego 5, 25-369 Kielce, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sebastian","family":"Sadowski","sequence":"additional","affiliation":[{"name":"DHL Parcel Poland, ul. Osma\u0144ska 2, 02-823 Warszawa, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zbigniew","family":"Koruba","sequence":"additional","affiliation":[{"name":"Faculty of Mechatronics and Mechanical Engineering, Kielce University of Technology, Al. 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