Sentiment Analysis of Incoming Calls on Helpdesk

In the present dynamic and highly competitive environment, sentiment analysis of incoming calls to helpdesks has been established as a critical tool in the improvement of both customer satisfaction and operational efficiency. This paper presents the implementation of a framework for sentiment analysis intended to automatically classify customer emotions over the course of helpdesk interactions. For the interpretation of the audio from call recordings into written text, speech-to-text technology is applied, and services can be Google Speech API or IBM Watson. Preprocessing steps include noise removal and text normalization, followed by tokenization before further processing. Performance metrics including accuracy, precision, recall, and F1-score of the models are measured, thereby improving the performance. For real-time needs during helpdesk interactions, the system has also been interfaced with call management software, providing instant feed for sentiment analysis for guidance to agents and supervisors. The project involved such challenges as noisy and possibly unstructured audio data were being processed, imbalances in sentiment classes in datasets, scalability for large scale deployments, and ethical points of considerations like user private issues or biases in general that were avoided in the implementation. The results indicate that the offered framework achieves an accuracy above 85% in classifying sentiments, while at the same time, significantly improving helpdesk service operations by indicating important calls that need instant attention. This study marks the ability of sentiment analysis to revolutionize customer service by facilitating proactive, empathetic interaction that goes a long way to further customer satisfaction and favorable business results.