Faculty of Electrical Engineering, Automatic Control and Computer Science, Kielce University of Technology, Al. Tysiąclecia P.P. 7, 25-314 Kielce, Poland.
Institute of Educational Sciences, Pedagogical University in Kraków, ul. 4 Ingardena, 30-060 Cracow, Poland.
Sensors (Basel). 2022 Jul 15;22(14):5311. doi: 10.3390/s22145311.
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 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%).
在过去的几年中，用于联络中心系统的虚拟助手解决方案越来越受欢迎。虚拟助手的主要任务之一是识别客户的意图。需要注意的是，在对话中表达的实际意图通常也会受到伴随该对话的情绪的直接影响。不幸的是，科学文献尚未确定联络中心应用程序中哪些特定类型的情绪与他们执行的活动相关。因此，这项工作的主要目标是开发一个专门针对联络中心行业的虚拟助手。在进行的研究中，考虑了联络中心的语音和文本渠道，并考虑了以下情绪家族：愤怒、恐惧、幸福、悲伤与陈述的情感中性。获得的结果证实了所提出的分类的有用性-对于语音通道，使用卷积神经网络（准确性，67.5％;精度，80.3; F1-分数，74.5％）获得了最高效率，而对于文本通道，支持向量机算法被证明是最有效的（准确性，65.9％;精度，58.5％; F1-分数，61.7％）。
