Text As Data: Comp Linguistics

3 Credits · Level: Graduate

This course introduces students to the quantitative analysis of text as data. With the increasing availability of large-scale textual data – from government documents and political speeches to social media and online news – the potential to extract meaningful insights from text has expanded greatly. In this course, students will learn to transform text into data and apply it to social science questions and theories. Key topics include text representation, sentiment analysis, scaling text on ideological and policy dimensions, using machine learning for text classification, word embeddings and using Large Language Models (GPT, Bert, Llama, Gemini, etc..) for social science research. Although we will cover LLMs, the focus will be on applied methods and transfer learning rather than an in-depth coverage of building LLMs from scratch. The course includes hands-on exercises using real-world data to reinforce lecture content. By the end, students will have a toolkit for text analysis useful in roles as policy experts and computational social scientists. Students should have completed at least an introductory statistics course and have a basic understanding of probability, distributions, hypothesis testing, and linear models. Students are also expected to have experience working with R, the programming language and software environment of this course, and Python for the LLM's components of the course.