One of the most abundant sources of data available to social and political scientists today is text. Recent advances in Natural Language Processing (NLP) have spearheaded a text-as-data revolution, which has led social scientists to seek out new means of analyzing text data at scale. In this course, we will learn the intuition behind—and how to implement—different computational methods to process, analyze, and classify text. The course will cover Bag-of-Words (BoW) approaches, unsupervised methods, supervised and semi-supervised methods, and generative methods that use text as data, as well as how we can interpret the results obtained from applying these methods.