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.