The Mood Machine

The Mood Machine is a simple rule based text classifier. It tries to guess whether a short piece of text sounds positive, negative, or neutral based on lists of keywords. This lab gives you a hands on look at how basic systems work, where they break, and why even simple choices can create bias.

You will edit the code, run experiments, add data, and create a short model card reflection.

---

Repo Structure

├── dataset.py        # Starter word lists and example posts
├── mood_analyzer.py  # Core class with TODOs for you to implement
├── main.py           # Runner script for demos and interactive testing
└── model_card.md     # Template you will fill out after experimenting

---

Getting Started

1. Open this folder in VS Code.

2. Make sure your Python environment is set up.

3. Run the starter script:

   python main.py

If parts of the analyzer are not implemented yet, you will see helpful error messages that point you to the TODOs.

---

What You Will Do

During this lab you will:

• Implement the missing methods inside MoodAnalyzer.
• Add new positive and negative words.
• Expand the dataset with more example posts, including slang or emojis.
• Observe unusual or incorrect predictions.
• Compare your system to an AI tool such as ChatGPT.
• Fill out the model card template with your findings.

This activity helps you reason about how models behave, why they fail, and how design decisions influence fairness and accuracy.

---

Tips

• Start small. Get preprocess working before moving on to scoring.
• When debugging, print intermediate results to see what your code is doing.
• Ask an AI assistant to generate example posts, alternative keyword lists, or explanations of unfamiliar Python ideas.
• Try examples that break your system. Surprising cases teach you the most.