Text as Data: A New Framework for Machine Learning and the Social Sciences

Grimmer, Justin

ISBN 10: 0691207550 / ISBN 13: 9780691207551

Published by Princeton University Press, 2022

About this Item

This is an ex-library book and may have the usual library/used-book markings inside.This book has soft covers. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,800grams, ISBN:9780691207551.

Seller Inventory # 2919076

Bibliographic Details

• Title: Text as Data: A New Framework for Machine Learning and the Social Sciences
• Publisher: Princeton University Press
• Publication Date: 2022
• Language: English
• ISBN 10: 0691207550
• ISBN 13: 9780691207551
• Binding: Soft cover
• Condition: Good
• Seller catalogs:
  • Social Sciences

About this title

Synopsis

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 organized 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 analyze 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 Author

Justin Grimmer is professor of political science and a senior fellow at the Hoover Institution at Stanford University. Twitter @justingrimmer Margaret E. Roberts is associate professor in political science and the Halıcıoğlu Data Science Institute at the University of California, San Diego. Twitter @mollyeroberts Brandon M. Stewart is assistant professor of sociology and Arthur H. Scribner Bicentennial Preceptor at Princeton University. Twitter @b_m_stewart