Text Classification

Text classification is the task of assigning a predefined category (or label) to a piece of text. It is one of the most fundamental and widely used NLP tasks, powering applications from spam filtering to content moderation.

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Types of Text Classification

Type	Description	Example
Binary	Two classes	Spam vs Not Spam
Multi-class	Three or more classes (one label per document)	News category: Sports, Politics, Business, Tech
Multi-label	Multiple labels per document	A movie tagged as both "Action" and "Comedy"

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The Text Classification Pipeline

1. Collect and label data — Gather text samples with their categories
2. Preprocess — Clean, tokenise, and normalise the text
3. Represent — Convert text to numerical features (TF-IDF, embeddings)
4. Train — Fit a classifier on the training data
5. Evaluate — Measure performance on held-out test data
6. Deploy — Use the model to classify new text

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Classical Approaches

Naive Bayes

Naive Bayes is a probabilistic classifier based on Bayes' theorem with the "naive" assumption that features are conditionally independent given the class.