Text classification assigns predefined category labels to spans of text -- documents, sentences, or questions -- for tasks such as sentiment analysis, topic labelling, and spam or hate-speech detection. The end-to-end pipeline runs from datasets and text preprocessing, through representation (bag-of-words and TF-IDF, through word embeddings, to contextual representations from transformers and large language models), to a classifier (Naive Bayes, SVM, KNN, or deep neural networks) and evaluation with precision, recall and F1. Learners see how each stage's choices trade off interpretability, data requirements and accuracy, and how deep learning folds feature engineering into training.
Text classification stands as a cornerstone within the realm of Natural Language Processing (NLP), particularly when viewed through computer science and engineering. The past decade has seen deep lea…