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Automated Petition Analysis and Categorization System Using Machine Learning for Efficient Grievance Redressal
Automated Petition Analysis and Categorization System Using Machine Learning for Efficient Grievance Redressal
Mr. Viswanath kani T
Ms. Hemalatha G
Ms. Keerthika S
Department of Computer Science
and Engineering
Vivekanandha college of
Engineering for Women
Namakkal, India
kaniviswanath@gmail.com
hemalathaguna123@gmail.com
keerthikasaravanan52@gmail.com
Abstract: The Petition Analysis and Categorization System leverages machine learning and automation to streamline petition handling for government agencies and businesses. By utilizing BERT for department classification, Random Forest for urgency detection, and K-Means clustering to identify recurring petitions, the system minimizes redundancy and enhances efficiency. Additionally, LSTM models monitor unresolved cases, ensuring timely follow-ups. Real-time notifications are facilitated through the Twilio API, keeping petitioners and officials updated on petition status. To optimize data management, petitions are stored as images in MySQL, enabling quick retrieval and classification. With a user-friendly web interface, the system ensures transparency, accountability, and faster resolution of public grievances while reducing manual effort and improving response times.
