MahdiAhmed8/Complaint-Intel

Arabic and French complaint intelligence for Tunisian companies # Sawt — Tunisian Complaint Intelligence An inspectable Arabic/French NLP baseline for Tunisian companies. Sawt detects language, classifies complaint topic, sentiment and urgency, finds recurring operational problems, routes work to departments, and produces a short management brief. The baseline deliberately uses word + character TF‑IDF and logistic regression. It makes no external AI/API calls. ## Run locally ```powershell python -m venv .venv .venv\Scripts\Activate.ps1 pip install -r requirements.txt streamlit run app.py ``` The app starts with a balanced synthetic bilingual dataset so every feature is immediately usable. Upload a CSV from the sidebar to classify real data. The only required column is `text`; optional columns are `date`, `product`, and `location`. If `topic`, `sentiment`, `urgency`, and `language` are present, they are treated as existing labels. ## Train from labelled company data The CSV must contain `text`, `topic`, `sentiment`, and `urgency`. Use the six topic names from `complaint_intelligence/constants.py` and consistent sentiment/urgency labels. ```powershell python train.py --data data/company_complaints.csv --output models/baseline.joblib ``` Training prints held-out accuracy and macro F1. The Streamlit app trains its cached demo model in memory; `train.py` is the reproducible persistence path for deployment. ## Evaluation approach - Fixed 75/25 stratified train/test split (stratified by topic) - Macro F1 and accuracy for every task - Per-class report and confusion matrix - Balanced logistic regression to reduce majority-class bias - Confidence below 55% triggers a human-review notice For a production evaluation, replace the synthetic data with reviewed historical complaints, deduplicate near-identical messages before splitting, and preferably use a time-based holdout. Monitor macro F1 by language, product, and location; tune the confidence threshold from actual review costs. ## Project structure ```text app.py …