Text Analytics for Solving Employee Helpdesk Challenges

Abstract

Employee helpdesks are a vital component of modern organizations, addressing concerns ranging from IT issues to HR-related queries. However, inefficiencies such as delayed responses, misclassification of tickets, and high operational costs often plague these systems. This study investigates the use of text analytics, particularly Natural Language Processing (NLP) and machine learning (ML), to address these challenges. By leveraging these techniques, organizations can automate ticket classification, analyze employee sentiment, and predict recurring issues. This manuscript explores the application of text analytics in helpdesk systems through a literature review, a methodological framework, and empirical results from a case study. Findings indicate that text analytics significantly improves efficiency, accuracy, and employee satisfaction, paving the way for smarter and more responsive helpdesk operations.