feat(intermediate): add student feedback sentiment analyzer starter project

[Hitesh-XS]

Closes #8

📌 Summary

Adds a new intermediate starter project under intermediate/student-feedback-sentiment-analyzer/. This project trains, compares, and evaluates classic Machine Learning models to classify course feedback into Positive, Neutral, or Negative categories, and performs aspect-based sentiment analysis (which part of the course each opinion is about).

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🛠️ What's Included

1. train_and_evaluate.py

• Data Generation: Template-generated synthetic data (~900 comments across 3 balanced classes). The 16 hand-written campus comments are kept test-only as an independent gold set. A slice of phrase templates is also held out from training to measure generalization to unseen wording.
• Preprocessing: Custom text normalization with negation marking ("not helpful" → not_helpful, scope ends at punctuation or "but"), contraction expansion, and hybrid TF-IDF features: word n-grams (1–2) plus character n-grams (3–5).
• Model Comparison: Evaluates Logistic Regression, Calibrated Linear SVC, Complement Naive Bayes, and a soft-voting ensemble using 5-fold Stratified Cross-Validation (macro-F1). Optional --tune grid-searches Logistic Regression (n-grams, C).
• Evaluation Metrics: Evaluates on three test sets (in-distribution, unseen phrasing, hand-written gold) with Precision, Recall, Macro F1, Accuracy, and a Confusion Matrix. Metrics can be exported with --metrics-out.
• Theme & Aspect Extraction: Top n-grams per class, most influential words from Logistic Regression weights, and clause-level aspect-based sentiment (lectures, labs, assignments, instructor, exams, materials, pace). Mixed comments like "labs were helpful, but deadlines were too tight" get a sentiment per aspect, plus confidence and low-confidence flags.
• Model Persistence: --save / --load to reuse a trained model, and --predict "..." to analyze a single comment.
• Bring Your Own Data: --data feedback.csv (columns text,label) trains on real comments.
• Interactive Mode: Optional --interactive CLI flag for testing custom comments in real time.

2. Supporting Files

• requirements.txt: Standard dependencies (scikit-learn, pandas, numpy, joblib).
• README.md: Covers project architecture, evaluation methodology, setup instructions, usage examples, sample results, and system limitations.
• intermediate/README.md: Updated index linking to the new project.

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📊 Sample Results (default synthetic run, macro-F1)

Model	In-distribution	Unseen phrasing	Hand-written gold
Logistic Regression	1.00	0.79	0.94
Linear SVC (calibrated)	1.00	0.80	0.94
Complement NB	0.91	0.79	0.88
Ensemble (soft voting)	1.00	0.80	0.94

In-distribution and CV scores are optimistic because synthetic folds share phrase templates. The unseen phrasing and hand-written columns are the more honest generalization signals.

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⚠️ Known Limitations

• Vocabulary is limited by the synthetic data, so unseen words (e.g. "great") carry little signal. Training on real comments via --data is recommended.
• Aspect detection is keyword-based and heuristic. Sarcasm and long multi-sentence comments may be misread.
• Saved models must be loaded through train_and_evaluate.py (preprocessing functions are pickled by reference).

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🧪 How to Test

cd intermediate/student-feedback-sentiment-analyzer
pip install -r requirements.txt

# Run full pipeline (Train, Cross-Validate, Evaluate, Feature Weights, ABSA samples)
python train_and_evaluate.py

# Save a model, then predict a single comment
python train_and_evaluate.py --save model.joblib
python train_and_evaluate.py --load model.joblib --predict "Labs were helpful but the deadlines were too tight"

# Optional: hyperparameter tuning, custom data, interactive mode
python train_and_evaluate.py --tune
python train_and_evaluate.py --data feedback.csv
python train_and_evaluate.py --interactive

✅ Checklist

• Runs end to end with default settings
• Includes README.md and requirements.txt
• Updated intermediate/README.md index
• No external datasets or network access required

[github-actions]

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