Vocabulary & Feature Extraction in NLP — Step-by-Step Guide
In this video, you’ll learn one of the most fundamental steps in Natural Language Processing (NLP) — how to represent text as numbers using Vocabulary Building and Feature Extraction.
You’ll see:
✅ What a vocabulary is in NLP
✅ How to represent text data as numerical vectors
✅ The concept of sparse representation
✅ How to use CountVectorizer in scikit-learn to create a Bag of Words model
✅ A hands-on Python example that converts tweets into feature vectors
By the end of this tutorial, you’ll understand how machines transform words into numbers — the foundation of all text classification, sentiment analysis, and machine learning for NLP tasks.
📘 Topics Covered:
Vocabulary in NLP
Feature Extraction & Sparse Representation
Bag of Words model
CountVectorizer in Python
Feature matrix and interpretation
💡 Python Libraries Used:
scikit-learn, pandas
🔗 Explore Related Tutorials:
What is NLP? (Chapter 1)
Sentiment Analysis using Logistic Regression
👉 Try the code on GitHub:
dr-mushtaq/natural-language-processing-projects-python
NLP vocabulary building #featureextraction # nlp_master # bag of word #CountVectorizer tutorial