A guide for using computational text analysis to learn about the social world. Text as Data shows how to combine new sources of data, machine learning tools, and social science research design to develop and evaluate new insights.

Mapping Texts offers a practical introduction to computational text analysis with step-by-step guides on how to conduct actual text analysis workflows in the R statistical computing environment. The focus is on social science questions and applications.

The Dangerous Art of Text Mining celebrates the bold new research now possible because of text mining. However, this book also presents a warning: without help from the humanities, data science can distort the past and lead to perilous errors. The book opens with a rogue's gallery of errors, then tours the ground-breaking analyses that have resulted from collaborations between humanists and data scientists. It ends with an impassioned vision of what text mining in defence of democracy would look like, and why humanists need to be involved.

How does a literary historian end up thinking in terms of z-scores, principal component analysis, and clustering coefficients? The essays in Distant Reading led to a new and often contested paradigm of literary analysis. In presenting them here Franco Moretti reconstructs his intellectual trajectory, the theoretical influences over his work, and explores the polemics that have often developed around his positions.