Sentiment Analysis of Incoming Calls on Helpdesk

Abstract

This study presents an advanced sentiment analysis system designed to evaluate and interpret emotional tones expressed during incoming calls to a helpdesk. The system captures real-time conversations between helpdesk agents and customers, processes the audio into transcribed text, and applies natural language processing (NLP) techniques to assess the underlying sentiment. Each interaction is assigned a sentiment score within a standardized range of -1 (strongly negative) to +1 (strongly positive), with 0 indicating a neutral stance. Beyond simple sentiment detection, the system also gauges the intensity of emotional expressions, such as frustration, satisfaction, confusion, or urgency. To scale support operations, the system is integrated with a comprehensive visual analytics dashboard, enabling supervisors and quality assurance teams to monitor multiple helpdesk agents simultaneously. The dashboard displays real-time sentiment trends, emotional spikes, and historical summaries, helping teams to quickly identify critical incidents, recurring negative interactions, or patterns that require managerial attention. Furthermore, the system supports multi-language processing, enabling global helpdesk operations to uniformly assess customer satisfaction across different regions