Scalable Customer Support Ticket Analysis Using Hybrid NLP and Ensemble-Based Classification Models

Abstract

Customer support centers generate large volumes of textual data in the form of support tickets, reflecting customer issues, service priorities, and satisfaction levels. Analyzing this unstructured data is essential for improving service quality and operational efficiency. However, traditional approaches rely on manual processing or basic machine learning techniques using shallow features such as bagof-words and Term Frequency–Inverse Document Frequency (TF-IDF), which fail to capture contextual meaning and semantic relationships. These limitations result in reduced accuracy, poor scalability, and challenges in handling multiple prediction tasks, ultimately affecting timely customer service. This study presents an advanced analytical framework that integrates natural language processing with machine learning techniques to address these challenges. The textual data is preprocessed using standard NLP techniques, including tokenization, stopword removal, and lemmatization, to clean and normalize input. Transformer-based feature extraction is applied using eXtreme Language Network (XLNet) to generate contextual embeddings that capture deeper semantic information. These embeddings provide a rich numerical representation, enabling improved analysis and prediction. Multiple machine learning models, including Quadratic Discriminant Analysis (QDA), Linear Discriminant Analysis (LDA), Histogram-Based Gradient Boosting (HGB), Stochastic Gradient Descent (SGD), and Nearest Centroid (NC), are trained to perform multi-target classification. The system predicts key attributes such as ticket priority, customer satisfaction rating, and resolution status. Additionally, exploratory data analysis techniques are used to visualize patterns and trends. This integrated approach enhances prediction accuracy, supports scalability, and enables efficient, data-driven customer support management.