What is text classification and why is it important?
Categorizing text is the first step to understanding language at scale.
How It Works:
Text classification assigns labels (like ?spam? or ?positive?) to documents by feeding tokenized text into a trained model that predicts the most likely category.
Key Benefits:
- Automates sorting of large volumes of text
- Enables sentiment analysis, topic tagging, and filtering
- Provides structured insights from unstructured data
Real-World Use Cases:
- Email spam detection
- Customer review sentiment scoring
FAQs
Do I need labeled data?
Can models handle multiple labels?
.png)
.svg)
