Text Classification

Text classification is one of the most fundamental tasks in NLP. Given a document and a set of categories, the goal is to assign the correct label or set of labels. Common applications include spam detection (spam vs. ham), sentiment analysis (positive vs. negative), topic classification (sports, politics, technology), intent detection in chatbots, and content moderation.

Classical approaches represented text as bag-of-words or TF-IDF vectors and trained logistic regression, Naive Bayes, or SVM classifiers. These are fast and interpretable but ignore word order and cannot capture semantic nuance. Convolutional and recurrent neural networks applied to word embeddings improved accuracy on longer documents. Transformer-based models fine-tuned on labeled data now represent the state of the art and have dramatically lowered the data requirements for strong performance.

Text classification can be binary (two classes), multiclass (one of many classes), or multilabel (multiple classes simultaneously). Evaluation uses accuracy, precision, recall, and F1 score, with macro or micro averaging depending on whether all classes should be weighted equally. Data quality and label consistency are often larger bottlenecks than model architecture in production classification systems.