https://at-ceu.studyguide.timeedit.net/modules/INTR5077?type=CORETextual data is everywhere: social-media content, news and blog posts, political speeches, official documents, policy papers - and increasingly, it serves as a key source of data for social-science research. This course gives students the tools to treat text as data: to collect, preprocess, analyze, and interpret large or complex text corpora using computational methods. Through hands-on work in R, students will learn how to transform raw text into analyzable data, apply techniques like keyword analysis, sentiment scoring, topic modeling, and embeddings, and visualize and interpret the results in a way suitable for academic research. By the end of the course, they will be equipped to design and execute their own text-analysis projects.This is an applied introductory course: the focus is on practical skills and empirical work rather than mathematical derivations or advanced statistical theory behind the methods. Thus, if you already have a background in text analysis and require a rather more advanced knowledge, please consult with the other available courses.