Bag of Words (BoW) is a fundamental concept in Natural Language Processing (NLP) and Machine Learning. It's a simple yet powerful way to convert text into numbers that computers can understand and analyze.
Key Concepts:
- Tokenization: Breaking text into individual words
- Vocabulary: All unique words across your documents
- Word Counting: How many times each word appears in each document
- Order Doesn't Matter: We only care about frequency, not word position
🎯 Example:
Documents: ["I love pizza", "Pizza is great", "I love food"]
Vocabulary: [i, love, pizza, is, great, food]
BoW Matrix:
Doc 0: [1, 1, 1, 0, 0, 0] ← "I love pizza"
Doc 1: [0, 0, 1, 1, 1, 0] ← "Pizza is great"
Doc 2: [1, 1, 0, 0, 0, 1] ← "I love food"
🚀 Real-World Applications:
- Spam Detection: Email classification
- Sentiment Analysis: Positive/negative reviews
- Document Classification: News categorization
- Search Engines: Finding relevant documents
- Recommendation Systems: Content-based filtering
🎮 How This Game Works:
You'll see 3 text messages and a vocabulary of unique words. Your job is to manually count how many times each vocabulary word appears in each message. This hands-on practice helps you understand exactly how computers process text for machine learning!