CUSTOMER COMPLAINT TRACKING SYSTEM

Abstract: Customer complaints provide valuable information about problems in products, services, delivery, support, and customer experience. However, organizations often receive a large number of complaints through websites, email, social media, call centers, and mobile applications, making manual processing slow and difficult to manage. A customer complaint tracking system can improve this process by automatically collecting, classifying, prioritizing, routing, monitoring, and resolving complaints. This paper presents a review of recent approaches based on Natural Language Processing (NLP), Machine Learning (ML), Deep Learning (DL), sentiment analysis, topic modelling, and transformer-based models for intelligent complaint management. NLP techniques can convert unstructured complaint text into useful information, while machine learning algorithms can classify complaints according to category, urgency, department, or severity. Sentiment analysis can identify customer emotions and support priority assignment, whereas automated routing can forward complaints to the appropriate department. Recent research has also explored BERT and AI-enabled complaint-handling systems. The review identifies important research gaps related to multilingual complaints, limited labelled datasets, explainability, privacy, real-time tracking, integration with existing business systems, and human-AI collaboration. Future systems should combine NLP, explainable AI, large language models, sentiment analysis, automated escalation, and real-time dashboards to provide faster and more transparent complaint resolution.

Keywords: Customer Complaint Tracking, Complaint Management, Natural Language Processing, Machine Learning, Sentiment Analysis, Text Classification, BERT, Topic Modelling, Automated Routing, Customer Service.