Text Preprocessing
Clean and normalize raw text before any NLP task begins.
Run in Google Colab ↗Overview
Text preprocessing is the foundational step in any NLP pipeline. Raw text from real-world sources — tweets, reviews, transcripts — contains noise: inconsistent casing, punctuation, slang, and filler words. Preprocessing standardizes this input so downstream tasks (tokenization, vectorization, classification) work reliably.
💡 Why It Matters
Skipping preprocessing can degrade model performance significantly. For example, 'Running', 'running', and 'RUNNING' are the same word but treated as three different tokens without normalization. Slang like 'gonna' or 'u' can confuse models unless mapped to their standard forms.
Core Preprocessing Steps
Lowercasing
Converts all characters to lowercase to remove case-based duplication.
Punctuation Removal
Strips non-alphanumeric characters using regex to reduce noise.
Stopword Removal
Removes high-frequency, low-information words like 'the', 'is', 'at' using NLTK's stopword corpus.
Slang Normalization
Maps informal terms to standard equivalents using a custom dictionary (slang.txt in this repo).
🛠 Library Note
This module uses NLTK for stopword handling and Python's built-in `re` module for regex-based cleaning.
External Documentation
What You'll Learn
- Why raw text needs cleaning before analysis
- Lowercasing, punctuation removal, and stopword filtering
- Slang normalization using a custom slang.txt reference