AI Based Petition and Grievance System

Efficient grievance management is essential for ensuring transparency and responsiveness in modern e-governance systems. However, traditional approaches rely heavily on manual processing, leading to delays, misclassification, and inefficiencies when handling large volumes of complaints. This paper proposes an intelligent Natural Language Processing (NLP)-based grievance classification and routing system using the DistilBERT model. The system automatically analyzes textual complaints, classifies them into appropriate departments, and predicts their severity levels. A web-based interface is developed to facilitate user interaction, while a backend framework integrates preprocessing, model inference, and automated notification for real-time routing. The use of DistilBERT enables high classification accuracy with reduced computational complexity, making the system suitable for deployment in resource-constrained environments. Experimental results demonstrate that the proposed approach outperforms traditional machine learning methods in terms of accuracy, efficiency, and scalability. The system significantly reduces manual effort and improves response time, thereby enhancing the overall effectiveness of grievance management systems.

Natural Language Processing, DistilBERT, Grievance Classification, Text Mining, E-Governance, Deep Learning, Automated Routing, Transformer Models

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