Text classification is the process of categorizing text data into predefined labels or categories. This is achieved by training machine learning models on labeled datasets, where the model learns to associate specific patterns or features in the text with particular labels.
Common applications of text classification include spam detection in emails, sentiment analysis, topic categorization, and language detection. For example, a text classification model can be used to automatically sort news articles into categories like politics, sports, and entertainment. Text classification relies on techniques like supervised learning and deep learning, using models such as Naive Bayes, Support Vector Machines (SVM), or neural networks.
By automating the classification of text, businesses can streamline workflows, enhance user experience, and derive insights from large volumes of unstructured text data.