Text Classification

Text classification assigns one or more predefined category labels to a piece of text. Examples include spam detection, sentiment analysis, topic categorization, and intent recognition. Classical pipelines vectorize text with bag-of-words or TF-IDF features and train Naive Bayes or logistic regression classifiers. Modern approaches fine-tune transformer encoders on labeled examples. Evaluation requires careful attention to class imbalance, since accuracy can be misleading when one class dominates the dataset.