Text Analysis with R for Students of Literature

  • Matthew L. Jockers

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Microanalysis

    1. Front Matter
      Pages 1-1
    2. Matthew L. Jockers
      Pages 3-10
    3. Matthew L. Jockers
      Pages 11-23
    4. Matthew L. Jockers
      Pages 25-28
    5. Matthew L. Jockers
      Pages 29-46
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      Pages 47-56
  3. Mesoanalysis

    1. Front Matter
      Pages 57-57
    2. Matthew L. Jockers
      Pages 59-67
    3. Matthew L. Jockers
      Pages 69-72
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      Pages 73-80
    5. Matthew L. Jockers
      Pages 81-87
    6. Matthew L. Jockers
      Pages 89-98
  4. Macroanalysis

    1. Front Matter
      Pages 99-99
    2. Matthew L. Jockers
      Pages 101-117
    3. Matthew L. Jockers
      Pages 119-133
    4. Matthew L. Jockers
      Pages 135-159
  5. Back Matter
    Pages 161-194

About this book

Introduction

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.

Keywords

Computational Literary Studies Corpus Linguistics and R Digital Humanities Linguistic Computing Programming and Literature R Text Analysis Text Classification Text Clustering Text Mining

Authors and affiliations

  • Matthew L. Jockers
    • 1
  1. 1.Department of EnglishUniversity of NebraskaLincolnUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-03164-4
  • Copyright Information Springer International Publishing Switzerland 2014
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-319-03163-7
  • Online ISBN 978-3-319-03164-4
  • Series Print ISSN 2199-0956
  • Series Online ISSN 2199-0964
  • About this book