# Emotion Recognition on Call Center Voice Data
###### Abstract
This includes various forms, such as text messages, emails, online conversation forums, reviews of products, blogs on the web, and social media platforms like Twitter, YouTube, Instagram, or Facebook. Emotion identification in text has various applications in industries such as business, education, psychology, and others, where there is a requirement to comprehend and analyze emotions [4]. Implicit emotion recognition in text is a challenging job in natural language processing (NLP) that requires natural language understanding (NLU). Text emotion recognition can be categorized into various levels: document, paragraph, sentence, and word level. The challenge arises at the sentence level when emotions are conveyed through the meanings of words and their connections. As the level progresses, the problem becomes more intricate. However, it is important to note that not all thoughts are conveyed with clarity, as they may involve the use of sarcam, irony, and metaphors. An emotion can be successfully recognized using a keyword-based technique. Nevertheless, the occurrence of an emotion keyword does not consistently correspond to the conveyed sentiment. For instance, the words \"Do I seem happy to you!\" and \"I am devoid of happiness\" contain the emotion term \"happy\" but fail to convey that specific emotion. In addition, a statement can convey emotion even without the use of an explicit emotion word. Scientists have been motivated to develop and apply automatic algorithms to identify emotional expressions to enhance intelligent human-machine interaction [5; 6]. Consequently, the discipline of affective computing has garnered significant interest from researchers over the past decade due to its diverse application areas, including multimodal human-computer interaction, security, education, health care, advertising, and marketing [7; 8; 9]. In the field of marketing and advertising, call centers are key elements for businesses. A call center is a group of customer service experts that handle telephone inquiries from potential or existing customers on a company's offerings or products. Some call centers prioritize customer pleasure and provide help, while others emphasize sales growth, lead creation, and customer acquisition. Call centers are essential for generating a positive client experience. Hence, they must consistently provide excellent service to foster relationships. Consequently, call center staff must possess extensive expertise, exhibit patience, and demonstrate helpfulness when engaging with clients [10]. Since call center employees are the first to interact with customers, recognition of customer emotions is essential for call center responders to respond appropriately and adequately. At this point, it would be better to use emotion recognition systems and direct the customer to a more or less experienced call center employee, depending on their emotional state, in terms of customer experience, the reaction of the call center employee, and company satisfaction. Currently, individuals are increasingly relying on computers to perform their daily tasks, leading to a growing need for improving human-computer interactions. A computer's capacity to recognize and generate emotions is impeded by a lack of essential knowledge. As a result, a significant amount of research has been conducted on the identification of emotions [11; 12; 13; 14; 15; 16; 17]. In this article, we conducted a study on emotion recognition on incoming customer calls to call centers, which plays a vital role in terms of customer experience and company satisfaction. This study aims to analyze the customer's emotion on an incoming customer call and to inform the call center employee who answers the call about the emotional state of the customer call according to the customer's emotional state. The fact that the customer's emotional state is known by the call center employees who answer the call is of great importance in terms of their sensitivity and performance in handling the call.