This chapter covers text classification, starting with methods based on statistical rules, which require manual feature selection. It then discusses machine learning approaches like support vector machines and decision trees, which automate feature selection but struggle with context and semantics. The chapter shifts to deep learning techniques, such as CNNs, RNNs, and graph neural networks (GNNs), which improve text classification by better capturing deep semantic features. Finally, it includes a practical example, detailing the process from data collection to evaluation, demonstrating the real-world application of these classification methods.