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Business Process Architecture for Sentiment Analysis on Speech Data

Asli Terim1,
Sumeyye Nur Çağlayan2,
Aytaç Kıvılcım3,
Mehmet Aktaş4
1Procat R&D Center
2Procat R&D Center
3Procat R&D Center
4Yıldız Technical University
Published:December 31, 2022
DOI: 10.56038/oprd.v1i1.211
Vol. 1, No. 1 · pp. 307–320

Abstract

Call Centers are the principal point of product and service providers, where they influence the customers. The fluctuations in the emotional states of the call center personnel directly affect the customers. These fluctuations may cause positive/negative results for the company in places where customer interaction is intense. Today, the supervision and evaluation of the activities of the agent, who is in contact with the customers, is essential in measuring and increasing the quality of the service.The system of rewarded bonuses is a way to encourage the employee. However, in the last decades, we have also observed that the emotional state´s effects are essential in the employee's performance. At present, analyzing, determining, and understanding agents' emotional states and work performance is highly necessary. This project has been started to measure the customer representatives´ emotional state and activities. This project addresses the need to evaluate customer representatives that work at Call Centers. Within the context of this research, we predict the emotional state of the customer representative while dialing in with the customer. According to the prototype software of the proposed methodology, customer representatives´ emotional situations on the dials are convenient to transfer as data to the Performance Evaluating Systems. With this project, it will be possible to score customer representatives according to their emotional states in the calls evaluated in quality evaluation and performance measurements, as well as personal support inferences for the personnel.

Keywords
Speech DataSentiment AnalysisMachine LearningCall CenterArtificial IntelligenceSpeech Recognition Methods

References

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Cite This Article
Terim, A., Çağlayan, S. N., Kıvılcım, A., Aktaş, M. (2022). Business Process Architecture for Sentiment Analysis on Speech Data. *Orclever Proceedings of Research and Development*, 1(1), 307-320. https://doi.org/10.56038/oprd.v1i1.211

Bibliographic Info

JournalOrclever Proceedings of Research and Development
Volume1
Issue1
Pages307–320
PublishedDecember 31, 2022
eISSN2980-020X