Text classification is a task in natural language processing (NLP) that involves assigning text data to one or more predefined categories or labels. It is a way of automatically organizing and categorizing text data based on its content, and it is commonly used for tasks such as spam detection, sentiment analysis, and topic modeling.
Text classification algorithms are trained on a labeled dataset, where each piece of text is associated with a predefined category or label. The algorithm uses this training data to learn the characteristics and features of the different categories, and then uses that learning to classify new text data.
There are different approaches to text classification, including rule-based approaches, which use a set of predefined rules to classify text; and machine learning-based approaches, which use statistical models and algorithms to learn from labeled data and make predictions about the class of new text.
Overall, text classification is an important tool in natural language processing, and it is a widely used technique for organizing and categorizing text data. It can be a valuable resource for businesses, researchers, and other organizations looking to automatically classify and organize text data for a variety of purposes.