Text Classification is a process in natural language processing (NLP) that involves categorizing text into predefined groups or classes. This technique is commonly used in various applications, such as spam detection in emails, sentiment analysis in social media, and topic labeling in news articles. By analyzing the content of the text, algorithms can determine which category best fits the given input. The process typically involves training a model on a labeled dataset, where each piece of text is associated with a specific category. Once trained, the model can then classify new, unseen text based on the patterns it learned during training. Popular algorithms for Text Classification include Support Vector Machines, Naive Bayes, and Deep Learning methods.