Text Classification Advanced

Text classification is the task of assigning one or more predefined categories to text documents. Advanced approaches use pre-trained transformer models (BERT, RoBERTa, DistilBERT) fine-tuned on task-specific data. Applications include spam detection, sentiment analysis, content moderation, search ranking, and document organization. Advanced text classification handles realistic challenges: imbalanced classes (99% of emails are not spam), multi-label scenarios (one document fits multiple categories), domain shifts (training on news, predicting on social media), and production constraints (latency, cost).