Text Classification

term_id: text_classification

Category: application_paradigms

Definition

Text classification is a supervised learning task where algorithms assign predefined categories to unstructured text data. Common techniques include Naive Bayes, Support Vector Machines, and Deep Learning models like LSTMs or Transformers. Applications range from sentiment analysis and spam detection to topic labeling and intent recognition, forming a foundational component of Natural Language Processing systems.

Summary

The process of categorizing text into organized groups based on its content or semantic meaning.

Key Concepts

  • Supervised learning
  • Labeling
  • Feature extraction
  • NLP

Use Cases

  • Sentiment analysis
  • Spam filtering
  • Topic modeling

Code Example

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from transformers import pipeline
classifier = pipeline("sentiment-analysis")

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