An Integrated Deep Learning Approach for Intelligent Customer Grievance Handling

Effectively managing customer complaints is a critical part of running any service-oriented business. How quickly and accurately grievances are addressed can directly affect customer satisfaction and loyalty. Traditional complaint handling systems, which often rely on manual processing or rigid rule-based methods, struggle to cope with the growing number and diversity of complaints coming from multiple digital channels. This paper proposes a holistic framework for automating customer grievance analysis using deep learning. The system combines multiple advanced techniques, including multimodal inputs, multilingual and cross- lingual processing, sarcasm and mixed sentiment detection, domain-specific adaptation, customer history context, hierarchical classification, aspect-based sentiment analysis, and explainable AI, into a single framework. By bringing these elements together, the framework aims to provide faster, more accurate, and more transparent complaint resolution, helping organizations respond efficiently to customers while building trust.