Emotion Recognition on Call Center Voice Data

Emotion recognition is a crucial aspect of human–computer interaction, particularly in the field of marketing and advertising. Call centers play a vital role in generating positive client experiences and maintaining relationships. As individuals increasingly rely on computers for daily tasks, there is a growing need to improve human–computer interactions. Research has been conducted on emotion recognition, in three main areas: facial expression-based, voice-based, and text-based. This study focuses on emotion recognition on incoming customer calls to call centers, which plays a vital role in customer experience and company satisfaction. The study uses real-life customer data provided by Turkish Mobile Operators to analyze the customer’s emotional state and inform call center employees about the emotional state. The model created in this research is a significant milestone for sentiment analysis in the Turkish language, demonstrating the ability to acquire fundamental patterns and categorize emotional expressions. The objective is to analyze the emotional condition of individuals using audio data received from phone calls, focusing on identifying good, negative, and neutral emotional states. Deep learning techniques are employed to analyze the results, with an accuracy value of 0.91, which is acceptable for our partner the “Turkcell Global Bilgi Pazarlama Danışmanlık ve Çağrı Servisi Hizmetleri” Incorporation.