Text Classification with Machine Learning

What is Text Classification?

Text classification assigns predefined categories to text documents. It's one of the most common and useful NLP tasks.

Use Cases

• Email spam filtering
• News categorization
• Support ticket routing
• Content moderation
• Language detection
• Intent classification

The Classification Pipeline

1. Data Collection

Gather labeled examples for each category.

2. Preprocessing

• Cleaning text
• Tokenization
• Normalization

3. Feature Extraction

• Bag of Words
• TF-IDF
• Word embeddings
• Sentence transformers

4. Model Training

• Naive Bayes
• SVM
• Random Forest
• Neural networks

5. Evaluation

• Accuracy
• Precision/Recall
• F1 Score
• Confusion matrix

6. Deployment

• API service
• Batch processing
• Real-time classification

Modern Approaches

Transfer Learning

Use pre-trained models like BERT for better results with less data.

Few-Shot Learning

Classify with minimal examples using LLMs.

Zero-Shot Learning

Classify without training examples using semantic similarity.

Best Practices

• Balance your dataset
• Use cross-validation
• Handle multi-label cases
• Monitor for drift
• Regular retraining

Conclusion

Text classification is a powerful tool for organizing and routing text data at scale.