Text Cleaning for NLP in Python | Tokenization, Stopwords, Lemmatization Explained
In this video, we explore *Text Preprocessing in Natural Language Processing (NLP)* using Python.
Text preprocessing is an essential step in NLP that helps clean and prepare raw text data before applying machine learning models. In this tutorial, we demonstrate the most important preprocessing techniques using a Python Jupyter Notebook.
📚 Topics covered in this video:
• Lowercasing text
• Tokenization (word and sentence tokenization)
• Removing stopwords
• Stemming using Porter Stemmer
• Lemmatization using WordNet Lemmatizer
• Building a complete text preprocessing pipeline
💻 Tools & Libraries Used:
• Python
• NLTK
• Jupyter Notebook
This tutorial is perfect for:
✔ Beginners learning NLP
✔ Students working on machine learning projects
✔ Data science enthusiasts
By the end of this video, you will understand how to preprocess text data efficiently for NLP tasks like sentiment analysis, chatbots, and text classification.
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