Text Classification
Automatically assigning texts to predefined categories using a machine learning model.
Text classification automatically assigns texts to categories – from spam detection to intent detection to content moderation, today mostly with transformer models.
Explanation
Text classification includes spam detection, topic labeling, intent detection, and sentiment analysis as a special case.
Marketing Relevance
Text classification automates content routing, support ticket triage, spam filtering, and content moderation.
Example
A support system automatically classifies incoming tickets by category (billing, technical, feature request) and priority.
Common Pitfalls
Class imbalance skews results. Domain shift between training and production. Multi-label vs single-label not clearly defined.
Origin & History
Naive Bayes was the first popular text classifier (1990s). SVMs dominated 2000-2012. BERT (2018) set new standards. Zero-shot classification with LLMs (2020+) enables classification without training.
Comparisons & Differences
Text Classification vs. Sentiment Analysis
Sentiment analysis is a special case of text classification with sentiment as the category.
Text Classification vs. Named Entity Recognition
Text classification gives one label per text; NER gives labels at the token/word level.