Hybrid Approaches to Emotion Recognition: Combining Acoustic Features with Textual Sentiment Analysis

Abstract

"With the covid outbreak online services became prominent for the smooth execution of day today services The quality of the service was a major concern to maintain the real rhythm of the services. Emotional recognition was a vital matrix considered in evaluation of service quality which was employed by many customer care services. Conventional approaches frequently fell short in comprehending the emotional nuances, resulting in less than optimum customer service encounters. There was a clear need for an advanced system that could accurately analyze both spoken and non-spoken signals in real-time. The goal was to increase the comprehension of client emotions and better the overall quality of customer service. In order to tackle this problem, a new and innovative approach was devised, which involved the integration of Natural Language Processing (NLP), Deep Learning, and Large Language Models (LLMs). This methodology enabled a comprehensive examination of spoken and textual interaction, hence providing a more profound comprehension of client emotions. Deep Learning algorithms analyzed both audio and written data, while Natural Language Processing (NLP) identified and retrieved important emotional cues from the text. LLMs enhanced this analysis by including context and semantic complexity, so assuring a comprehensive comprehension of the customer's emotional state. This versatile technique offered a complete instrument for identifying emotions, capable of managing the complex dynamics of human communication. The effectiveness of the system was assessed by rigorous testing on actual audio calls, which revealed the need of a consistent benchmark dataset for hybrid emotion identification methods. The empirical testing conducted in real-life scenarios proved the system's resilience and precision in detecting a diverse range of emotions, highlighting its capacity to transform customer support operations in contact centers. The testing validated that the integrated approach substantially enhanced the system's capacity to understand and address customer demands, hence facilitating more tailored and efficient customer support interactions. "