Text classification is a fundamental task in Natural Language Processing (NLP) that involves categorizing text into predefined classes. This tutorial will guide you through the basics of text classification, its applications, and simple implementation techniques.

Key Concepts

  • Definition: Assigning categories to text based on its content.
  • Common Use Cases:
    • Spam detection 📨
    • Sentiment analysis 😊😠
    • Topic labeling 📋
    • Intent recognition 🎯

Simple Implementation Steps

  1. Data Preparation

    • Collect and preprocess text data (tokenization, stopword removal, etc.).
    • Label the dataset with appropriate categories.
    data_preparation

  2. Model Selection

    • Choose algorithms like Naive Bayes 📊 or Deep Learning 🤖.
    • Use libraries such as scikit-learn or TensorFlow.
    naive_bayes
    deep_learning

  3. Training & Evaluation

    • Split data into training and testing sets.
    • Train the model and evaluate performance using metrics like accuracy or F1-score.
    • Fine-tune hyperparameters for better results.

  4. Deployment

    • Integrate the model into applications for real-time classification.
    • Monitor and update the model as needed.

Explore Further

For advanced techniques in NLP, check out our NLP Techniques Tutorial. 🚀