Luffy-2520/infotact-project1-grievance-nlp
AI-Powered Citizen Grievance & Sentiment Analysis System | NLP Project | Infotact Internship
SUMMARY AI summary by gpt-5-mini
An AI-powered system to automatically analyze citizen complaints: it predicts the responsible government department, classifies sentiment (Critical/Negative/Neutral/Positive), assigns an urgency score (0–100) and recommends priority actions. Intended for municipal grievance handlers, civic-tech teams, researchers or prototype evaluators working on automating complaint triage. Key features and implementation: - Models: TF-IDF features, Random Forest for department classification (53.33% accuracy), SVM for sentiment (66.11%), overall macro F1 53.33% - Stack: Python, NLTK, scikit-learn, FastAPI (REST endpoints: GET /, GET /health, POST /analyze) - Provides urgency scoring and a deployable API; project developed over a four-week internship with daily commits.
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Dates
| Created on GitHub | 2026-05-05 |
| Last push | 2026-05-09 |
| First seen here | 2026-05-09 |
| Last fetched | 2026-05-09 18:17 |
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