Text as Data

(3 Credits)

Course introduces students to quantitative texts-as-data analysis from an applied perspective, with a focus on political science. Course covers, inter alia, metrics of association between texts, burstiness of words and concepts, measurement of complexity and readability, scaling of political texts, automatic event extraction, dictionary methods for estimating sentiment, clustering, Latent Semantic Analysis, machine learning applications, topic models and LDA. Course also includes special topics such as the estimation of personal characteristics from writings, 'stylometrics' and detection of false statements. Course assumes no prior knowledge of texts-as-data work, though it requires proficiency with programming languages such as R or Python, along with an understanding of elementary statistical theory and regression analysis.