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Mapping Texts is the first introduction to computational text analysis that simultaneously blends conceptual treatments with practical, hands-on examples that walk the reader through how to conduct text analysis projects with real data. The book shows how to conduct text analysis in the R statistical computing environment--a popular programming language in data science.
List 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
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.
Summary
Mapping Texts is the first introduction to computational text analysis that simultaneously blends conceptual treatments with practical, hands-on examples that walk the reader through how to conduct text analysis projects with real data. The book shows how to conduct text analysis in the R statistical computing environment--a popular programming language in data science.
Additional text
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.
