Text as Data

A guide for using computational text analysis to learn about the social world.

From social media posts and text messages to digital government documents and archives, researchers are bombarded with a deluge of text reflecting the social world. This textual data gives unprecedented insights into fundamental questions in the social sciences, humanities, and industry. Meanwhile new machine learning tools are rapidly transforming the way science and business are conducted. 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.


Text as Data is organised around the core tasks in research projects using text representation, discovery, measurement, prediction, and causal inference. The authors offer a sequential, iterative, and inductive approach to research design. Each research task is presented complete with real-world applications, example methods, and a distinct style of task-focused research.

Bridging many divides — computer science and social science, the qualitative and the quantitative, and industry and academia Text as Data is an ideal resource for anyone wanting to analyse large collections of text in an era when data is abundant and computation is cheap, but the enduring challenges of social science remain.

• Overview of how to use text as data
• Research design for a world of data deluge
• Examples from across the social sciences and industry

About the Authors

Justin Grimmer is professor of political science and a senior fellow at the Hoover Institution at Stanford University.

Margaret E. Roberts is associate professor in political science and the Halicioglu Data Science Institute at the University of California, San Diego.

Brandon M. Stewart is assistant professor of sociology and Arthur H. Scribner Bicentennial Preceptor at Princeton University.