Text Classification — Text Categorization
Also known as: text categorization, document classification, topic classification, metin sınıflandırma
Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.
Key highlights
- Automates labelling of large document collections that would be impractical to sort by hand.
- Adapts to many tasks — spam detection, topic classification, and similar categorization problems — through the same supervised pipeline.
- Learns domain-specific patterns directly from labelled examples rather than relying on fixed rules.
Intuition
This section is available to Pro members. Upgrade to Pro
How it works
This section is available to Pro members. Upgrade to Pro
When to use it
Use text classification when you have text data sorted into predefined categories and a labelled training set to learn from. It needs at least roughly 100 labelled documents, and a balanced class distribution is preferred so the classifier can learn each category. With fewer than about 100 documents the classifier tends to overfit, so a zero-shot approach is safer; with no text data at all the method cannot run and general classification on categorical variables is the alternative.
Strengths & limitations
- Automates labelling of large document collections that would be impractical to sort by hand.
- Adapts to many tasks — spam detection, topic classification, and similar categorization problems — through the same supervised pipeline.
- Learns domain-specific patterns directly from labelled examples rather than relying on fixed rules.
- Requires labelled training data, which can be costly to produce.
- A balanced class distribution is preferred; skewed classes make some categories hard to learn.
- Needs a reasonably sized corpus (around 100 documents or more) to avoid overfitting.
Common pitfalls
This section is available to Pro members. Upgrade to Pro
Applications
This section is available to Pro members. Upgrade to Pro
Frequently asked
How much labelled data do I need?
Plan for at least roughly 100 labelled documents. Below that the classifier tends to overfit the small training set, so a zero-shot classification approach is the safer choice.
Does class balance matter?
Yes. A balanced class distribution is preferred so the classifier can learn each category reliably. With skewed classes, judge performance per category rather than relying on overall accuracy.
What if I have no text data?
Text classification cannot run without text. If your variables are categorical instead, use general classification methods on that structured data.
How is it different from document clustering?
Text classification is supervised: it assigns documents to predefined categories learned from labelled examples. Document clustering is unsupervised and groups similar documents without predefined labels.
Sources
- 1.Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.
- 2.Aggarwal, C. C. & Zhai, C. (2012). Mining Text Data. Springer.ISBN 978-1-4614-3222-7
You have read it. What now?
Cite this page
ScholarGate. (2026, June 1). Text Classification. ScholarGate. https://scholargate.app/text-mining/text-classification