Text Preprocessing

Text preprocessing is the essential first step in any NLP pipeline. Raw text data is noisy, inconsistent, and unstructured. Before a machine learning model can work with text, it must be cleaned, normalised, and transformed into a consistent format.

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Why Preprocess Text?

Raw text contains many sources of noise:

Issue	Example
Mixed case	"Hello" vs "hello" vs "HELLO"
Punctuation	"great!" vs "great"
Stop words	"the", "is", "and" — frequent but low information
Inflections	"running", "runs", "ran" — same root meaning
Special characters	URLs, HTML tags, emojis
Whitespace	Extra spaces, tabs, newlines

Tip: The preprocessing steps you apply depend on your task. For sentiment analysis, you might keep exclamation marks (they indicate emphasis). For topic modelling, you would remove them.

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The Preprocessing Pipeline

A typical text preprocessing pipeline follows these steps: