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 statistica...

Ausführliche Beschreibung

Bibliographische Detailangaben

Hauptverfasser:	Stoltz, Dustin S. (VerfasserIn), Taylor, Marshall A. (VerfasserIn)
Format:	Buch
Sprache:	English
Veröffentlicht:	Oxford Oxford University Press 2024
Schriftenreihe:	Computational social science series
Zusammenfassung:	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. Cover -- Advance Praise for Mapping Texts -- Mapping Texts: Computational Text Analysis for the Social Sciences -- Copyright -- Dediaction -- Contents -- Preface -- What You Will Learn -- What We Left Out -- Acknowledgments -- Part I: Bounding Texts -- 1: Text in Context -- What Is Language? -- What Is Text? -- 2: Corpus Building -- Texts Are Not People -- Balance, Range, and Representativeness -- Text Metadata -- Authors and Audiences -- Time and Location -- Domains and Media -- Text Data -- Languages and Dialects -- Genres and Topics -- Registers and Styles -- Redrawing Boundaries -- Part II: Prerequisites -- 3: Computing Basics -- Brass Tacks -- Coding Environments -- Data Objects, Types, and Structures -- Dialects of R -- Control Processes: Functions, Loops, and Apply -- Installing and Loading Packages -- Using Python in R -- Data Visualization -- Where to from Here -- 4: Math Basics -- The Fundamentals -- Comparing Vectors -- Dot Product -- Euclidean Distance and Cosine Similarity -- Correlation -- Regression -- Comparing Distributions -- Central Tendency -- Dispersion -- Types of Distributions -- Our Dear Friend, the Matrix -- Matrix Projection -- Vector Spaces and Singular Value Decomposition -- Graphs and Matrix Projection -- A Little Math Goes a Long Way -- Part III: Foundations -- 5: Acquiring Text -- Public Text Datasets -- Optical Character Recognition -- Automated Audio Transcription -- Application Programming Interfaces (APIs) -- Automated Web Scraping -- Legal and Ethical Side of Scraping -- Terms of Service -- Intellectual Property -- Individual and Organizational Privacy -- 6: From Text to Numbers -- Units of Analysis -- Tokenizing -- Chunking -- Document Features -- Sparsity -- Dedicated DTM Functions -- Token Distributions -- Zipf's Law and Herdan-heaps' Law -- Weighting and Norming -- Relative Term Frequency.
Beschreibung:	XV, 307 Seiten
ISBN:	9780197756881