# Emotion Recognition on Call Center Voice Data
###### Abstract  
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 Damsmanlk ve Cagn Servisi Hizmetleri\" Incorporation. emotion recognition; call center; deep learning; artificial intelligence +
Footnote †: journal: Medical  
[PERSON] introduced the three-dimensional theory of emotions in the late 19 th century. This theory categorizes emotions into three dimensions:  
1. Pleasure-Displeasure: This dimension encompasses subjective sensations of positivity or negativity. It encompasses happiness, contentment, sadness, and anger. 2. Tension-Relaxation: This dimension delineates the physiological arousal or tension associated with an emotion. Emotions encompass fear, excitement, relief, and serenity. 3. Excitement-Calm: Assesses the intensity of a feeling's excitement or tranquility. Joy, surprise, boredom, and fatigue are all components of it. [PERSON]'s tridimensional theory of emotions was an initial endeavor to categorize and comprehend human feelings. While it impacted subsequent research on emotions, contemporary psychological theories now encompass cognitive appraisal, social and cultural factors, and physiological responses [2]. To simplify matters, researchers commonly categorize emotions into two groups: \"positive\" and \"negative\", concerning properties of emotion that scientific researchers refer to as \"affective valence\". The hypothesis termed the 'affective modules' hypothesis is referred to here. The \"affective modes\" hypothesis suggests that a brain module can have diverse affective functions by being in multiple neurobiological states. This indicates that the emotional function or positive/negative value influenced by a brain module does not have to stay consistently steady but instead might vary dynamically in different circumstances [3]. Hence, a person's feelings could change rapidly between good and bad. Presently, individuals are depending progressively more on computers to carry out their everyday activities, hence intensifying the necessity to enhance human-computer connections. The absence of fundamental knowledge hinders a computer's ability to perceive and produce emotions. Consequently, extensive research has been carried out on the recognition of emotions. Emotion recognition can be categorized into three primary domains: facial expression recognition, speech-based emotion recognition, and text-based emotion recognition. Emotion recognition in text is the automated process of assigning an emotion to a selected text from a predetermined collection of emotion descriptors. Emotion recognition in text is crucial due to the prevalence of text as the primary means of human-computer interaction.