Abstract:

In this study, it is focused on the automatic evaluation of telephone conversations between call center employees and customers as positive or negative. The dataset used in the study include telephone conversations between call center employees and customers in the company. The data set contains 10411 three-second call center records; 5408 of them are positive records and 5003 of them are negative records that include arguments, anger and insults. In order to obtain meaningful features for emotion recognition from voice records, MFCC features were extracted from each call center records. The proposed CNN architecture is trained with MFCC features to classify call center records as positive or negative. The proposed CNN model showed 86.1% training accuracy, 77.3% validation accuracy and it achieved 69.4% classification accuracy on the test data. This study aimed to increase customer satisfaction by automatic analysis of conversations in call centers and notifying quality managers of negative records.

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