AI Appeals Processor: A Deep Learning Approach to Automated Classification of Citizen Appeals in Government Services

Researchers developed an AI Appeals Processor that uses deep learning to automatically classify government citizen appeals, achieving 78% accuracy with Word2Vec+LSTM architecture. The system reduces processing time by 54% compared to traditional manual processing that averages 20 minutes per appeal with only 67% accuracy.

• →Government agencies struggle with manual appeal processing that takes 20 minutes per case with 67% accuracy.
• →The AI system tested multiple approaches including SVM, fastText, LSTM, and BERT on 10,000 real citizen appeals.
• →Word2Vec+LSTM architecture provided the best balance of 78% accuracy and computational efficiency.
• →Processing time was reduced by 54% compared to traditional manual methods.
• →The system handles three primary categories (complaints, applications, proposals) across seven thematic domains.