What is Text Classification

How It Works

Text classification models encode input text into a representation vector and map it to class probabilities through a classification layer. Transformer-based models fine-tuned on labeled examples achieve state-of-the-art performance. The model learns patterns that distinguish categories, from simple topic assignment to nuanced intent detection and content moderation.

Technical Details

Modern approaches fine-tune pretrained language models (BERT, RoBERTa, DeBERTa) by adding a classification head on top of the [CLS] token representation. Multi-label classification uses sigmoid activation per class instead of softmax. Few-shot classification can be performed using prompt-based approaches with large language models. Evaluation uses accuracy, F1-score, precision, and recall, with macro vs micro averaging depending on class balance.

Best Practices

• Start with a pretrained model and fine-tune on at least 100 labeled examples per class
• Use stratified train-test splits to ensure all classes are represented in evaluation
• Apply class weights or oversampling for imbalanced datasets
• Use zero-shot classification with LLMs when labeled data is scarce
• Evaluate per-class metrics, not just overall accuracy, to catch underperforming categories

Common Pitfalls

• Using accuracy as the primary metric on heavily imbalanced datasets
• Not cleaning or normalizing text before classification, leading to noisy features
• Creating overlapping or ambiguous class definitions that confuse the model
• Evaluating on data that is too similar to training data, overestimating production performance

Advanced Tips

• Use text classification to auto-tag multimodal documents based on their text components
• Implement hierarchical classification for taxonomy-style category structures
• Combine text classification with visual classification for multimodal content categorization
• Apply active learning to efficiently select the most informative examples for labeling