Mapping Texts: Computational Text Analysis for the Social Sciences

by Dustin S. Stoltz, Marshall A. Taylor

Overview

Learn how to conduct a robust text analysis project from start to finish—and then do it again.

Mining is the dominant metaphor in computational text analysis. When mining texts, the implied assumption is that analysts can find kernels of truth—they just have to sift through the rubbish first. In this book, Dustin Stoltz and Marshall Taylor encourage text analysts to work with a different metaphor in mind: mapping. When mapping texts, the goal is not necessarily to find meaningful needles in the haystack, but instead to create reductions of the text to document patterns. Just like with cartographic maps, though, the type and nature of the textual map is dependent on a range of decisions on the part of the researcher. Creating reproducible workflows is therefore critical for the text analyst.

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, with data ranging from fake news and presidential campaigns to Star Trek and pop stars. The book walks the reader through all facets of a text analysis workflow—from understanding the theories of language embedded in text analysis, all the way to more advanced and cutting-edge techniques.

The book will prove useful not only to social scientists, but anyone interested in conducting text analysis projects.

Product Details

ISBN-13:	9780197756881
Publisher:	Oxford University Press
Publication date:	02/15/2024
Series:	Computational Social Science
Pages:	328
Sales rank:	957,704
Product dimensions:	9.30(w) x 7.10(h) x 1.20(d)

About the Author

Dustin S. Stoltz is an assistant professor of sociology and cognitive science at Lehigh University. His research explores how social structure, culture, and cognition shapes ideas and evaluations.

Marshall A. Taylor is an assistant professor of sociology at New Mexico State University. His research focuses on questions of cognition and measurement in the sociology of culture.

Table of Contents

Preface
Acknowledgements

I Bounding Texts
Ch. 1 Text in Context
Ch. 2 Corpus Building

II Prerequisites
Ch. 3 Computing Basics
Ch. 4 Math Basics

III Foundations
Ch. 5 Acquiring Text
Ch. 6 From Text to Numbers

IV Below the Document
Ch. 7 Wrangling Words
Ch. 8 Tagging Words

V The Document and Beyond
Ch. 9 Core Deductive
Ch. 10 Core Inductive
Ch. 11 Extended Inductive
Ch. 12 Extended Deductive
Ch. 13 Project Workflow and Iteration

Appendix
References

Editorial Reviews

"Stoltz and Taylor have managed to create a work that confidently takes even a beginner to a position of sophistication and technical virtuosity, leading to not only a practical mastery of cutting edge techniques, nor just that plus a clear understanding of the mathematical bases, but also gives the reader an intuitive feel for the larger social contexts that produce the text data analyzed, and all this without dropping a single equation in the reader's lap! A true gem." — John Levi Martin, Florence Borchert Bartling Professor of Sociology at The University of Chicago and author of Thinking Through Methods

"Language is pragmatic, language is habitual, language is relational. I have been waiting for a book like this. One that seamlessly integrates philosophy and theory with tools and reproducible examples and does so in a deeply sociological way. If you read this book, if you teach with this book, you and your students will have everything needed to successfully do computational text analysis in the social sciences. An extraordinary contribution." — Laura K. Nelson, Assistant Professor of Sociology at The University of British Columbia

"Packed with interesting examples, Mapping Texts reveals the exciting possibilities of computational text analysis for social science. It's one of those rare books that offers useful advice about both what to do and how to do it. I think many readers will love Stoltz and Taylor's decision to use no equations and illustrate key ideas with code in R. Mapping Texts will be helpful to anyone hoping to learn more about this dynamic and important area of research." — Matthew J. Salganik, Professor of Sociology at Princeton University and author of Bit by Bit: Social Research in the Digital Age

"This book is an excellent entry-point to modern ideas and tools for the quantitative analysis of textual data. Well-organized, approachable, and pragmatic, the book does a terrific job of showing why researchers find the ideas so interesting while also teaching the reader how to use the tools for themselves." — Kieran Healy, Professor of Sociology at Duke University and author of Data Visualization: A Practical Introduction

"Mapping Texts provides a timely and accessible foray into the evolving domain of computational text analysis, enriched with fascinating examples and cutting-edge techniques. The book is perfectly tailored for newcomers to text-based methods. Guiding readers through computational techniques without the complications of equations, the authors equip you with everything, including all the essential R code, to dive directly into text analysis. Moreover, the authors' approach is grounded in a philosophy of language and text that harmonizes the positivist and interpretive traditions, making the book an excellent fit for an especially wide range of social science and humanities scholars and students." — Anjali M. Bhatt, Assistant Professor of Business Administration at Harvard Business School

"Stoltz and Taylor's book is easily one of the clearest and most comprehensive introductions to computational text analysis written to date. With impressive dexterity, they take readers through the principles and workflows that are necessary for studying texts with computational techniques, never forgetting that words are complex and multifaceted cultural objects. In its style, organization, and pedagogical approach, this book is nothing short of an exceptional achievement." — Juan Pablo Pardo-Guerra, Associate Professor of Sociology the University of California, San Diego, and co-editor of The Oxford Handbook of the Sociology of Machine Learning

"This book could not be more welcome. Authored by two of the leading sociological researchers in the field of text analysis, it offers a comprehensive guide to state-of-the-art text analysis methods. But beyond just an introduction to methods, it provides a thoughtful and theoretically informed engagement about how we should think about, and interpret, the wealth of textual data that is now available. This is essential reading for anyone with an interest in computational social science." — Carly Knight, Assistant Professor of Sociology at New York University

From the Publisher