feat(intermediate): Add student feedback sentiment analyzer starter project #8

🎯 Goal

Build a starter project in intermediate/student-feedback-sentiment-analyzer that classifies student course feedback into sentiment categories (Positive, Neutral, Negative) and extracts common themes or keywords.

📌 Motivation

Analyzing student feedback at scale helps educational departments understand what teaching methods work and what areas require improvement. This intermediate project teaches students:

• Multi-class text classification.
• Handling subjective text nuances and ratings.
• Keyword/aspect extraction using frequency or simple n-gram techniques.

📋 Scope and Deliverables

1. Directory: Create intermediate/student-feedback-sentiment-analyzer/.
2. Components:
   • train_and_evaluate.py:
     • Synthetic or open educational feedback dataset (e.g., student course evaluation comments).
     • Text cleaning, tokenization, and vectorization.
     • Model comparison: Logistic Regression vs. Linear SVM or Naive Bayes.
     • Evaluation: Precision, Recall, Macro F1, and Confusion Matrix.
   • requirements.txt: Standard ML dependencies (scikit-learn, pandas, numpy).
   • README.md: Architecture overview, theory on multi-class sentiment, setup instructions, and sample runs.
3. Link to Blueprint: Update intermediate/README.md to reference the new project.

💡 Tips for Students

• Include realistic campus evaluation examples (e.g., "The lab demonstrations were very helpful, but the assignment deadlines were too tight").
• Keep code modular with functions for loading data, training, evaluating, and interactive testing.

🏷️ Help Wanted / Intermediate

Open to all students! Please comment below if you would like to work on this issue.