Having transformed raw text into structured features in the previous chapters, we now turn our attention to a core application: text classification. Many real-world NLP problems involve assigning predefined categories or labels to text documents. Examples include identifying spam emails, determining the sentiment of product reviews, or classifying news articles by topic.

This chapter concentrates on supervised learning techniques for building text classifiers. We will review standard algorithms like Naive Bayes, Support Vector Machines (SVM), and Logistic Regression, specifically focusing on their application to text data represented by features like TF-IDF or N-grams. You will learn how to:

• Prepare text features for input into classification models.
• Train and apply these models using common libraries.
• Evaluate model performance using appropriate metrics such as precision, recall, F1-score, and the confusion matrix.
• Implement robust evaluation strategies like cross-validation.
• Optimize model performance through hyperparameter tuning.
• Address challenges associated with imbalanced datasets, a frequent issue in text classification tasks.

By the end of this chapter, you will have the practical skills to build, evaluate, and refine text classification systems for various applications. We will conclude with a hands-on exercise to solidify these concepts.