What is Text Classification?
It is the process of analyzing the subject or content of a written text and sorting it into predetermined categories.
Overview
Text classification is a fundamental technique used in the field of natural language processing to make sense of written data. Artificial intelligence models determine which group a document belongs to by examining the words and context in the text. It is actively used in many applications such as sentiment analysis, spam detection, and topic tagging.
How it works
The system first breaks the text down into its words and converts them into numerical vectors. Then, the trained artificial intelligence model analyzes the patterns contained in the text. In the final stage, it assigns the category label that best fits the text.
Where it is used
It is frequently used in spam filtering for e-mail services, request routing in customer service, and content categorization on news sites.
Commonly confused with
It should not be confused with text generation: Text classification labels existing text and does not write new text.
Questions and answers
How much data is required for text classification?
While traditional methods require thousands of labeled examples, current artificial intelligence models can perform successful classification with very little data as well.
Is sentiment analysis a form of text classification?
Yes: Since it categorizes texts as positive, negative, or neutral, it is a specialized type of text classification.
Related terms
This explanation was written in plain language for TreScout with AI and machine-translated from the Turkish original · the Turkish version prevails. If something looks wrong or missing, write to hello@trescout.com. Read in Turkish →