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.
"
