Text Analysis with R for Students of Literature

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Text Analysis with R for Students of Literature is written with students and scholars of literature in mind but will be applicable to other humanists and social scientists wishing to extend their methodological tool kit to include quantitative and computational approaches to the study of text. Computation provides access to information in text that we simply cannot gather using traditional qualitative methods of close reading and human synthesis. Text Analysis with R for Students of Literature provides a practical introduction to computational text analysis using the open source programming language R. R is extremely popular throughout the sciences and because of its accessibility, R is now used increasingly in other research areas. Readers begin working with text right away and each chapter works through a new technique or process such that readers gain a broad exposure to core R procedures and a basic understanding of the possibilities of computational text analysis at both the micro and macro scale. Each chapter builds on the previous as readers move from small scale “microanalysis” of single texts to large scale “macroanalysis” of text corpora, and each chapter concludes with a set of practice exercises that reinforce and expand upon the chapter lessons. The book’s focus is on making the technical palatable and making the technical useful and immediately gratifying.

Author(s): Matthew L. Jockers (auth.)
Series: Quantitative Methods in the Humanities and Social Sciences
Edition: 1
Publisher: Springer International Publishing
Year: 2014

Language: English
Pages: 194
Tags: Statistics and Computing/Statistics Programs; Computational Linguistics; Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law

Front Matter....Pages i-xvi
Front Matter....Pages 1-1
R Basics....Pages 3-10
First Foray into Text Analysis with R....Pages 11-23
Accessing and Comparing Word Frequency Data....Pages 25-28
Token Distribution Analysis....Pages 29-46
Correlation....Pages 47-56
Front Matter....Pages 57-57
Measures of Lexical Variety....Pages 59-67
Hapax Richness....Pages 69-72
Do It KWIC....Pages 73-80
Do It KWIC (Better)....Pages 81-87
Text Quality, Text Variety, and Parsing XML ....Pages 89-98
Front Matter....Pages 99-99
Clustering....Pages 101-117
Classification....Pages 119-133
Topic Modeling....Pages 135-159
Back Matter....Pages 161-194