Text Analysis with R for Students of Literature

The author, Matthew L. Jockers, is Associate Professor of English and Director of the Nebraska Literary Lab at the University of Nebraska in Lincoln.  Jockers's text mining research has been featured in the New York Times, Nature, the Chronicle of Higher Education, Wired, New Scientist, Smithsonian, NBC News and many others. Jockers blogs about his research at www.matthewjockers.net.

"The aim of this book is ... to give the Literature students just the most basic tools needed to do some relatively straightforward textual analysis. ... Even though this is primarily a book intended for literature students, I would actually strongly recommend it to anyone interested in text mining, text analysis and natural language processing. It is a very gentle and approachable introduction to the whole world of textual analysis." (Bojan Tunguz, tunguzreview.com, July, 2015) "This is a well written book on the topic of Text Analysis. There is enough information to give you a good start using R. Followed by easy to understand details about text analysis. ... This is a good book to have if you are doing text analysis." (Mary Anne, Cats and Dogs with Data, maryannedata.com, August, 2014) "A remarkably well-crafted book that will allow students to get a quick start and progress toward quite sophisticated text mining tasks. ... exercises provided at the end of each chapter, with solutions at the end of the book, should serve well to help students solidify their knowledge and gain more confidence in their text mining skills. ... a great addition to the libraries of digital humanists and natural language enthusiasts who wish to expand their programming literacy ... ." (Denilson Barbosa, Computing Reviews, August, 2014)

• R Basics.- First Foray into Text Analysis with R.- Accessing and Comparing Word Frequency Data.- Token Distribution Analysis.- Correlation.- Measures of Lexical Variety.- Hapax Richness.- Do It KWIC.- Do It KWIC (Better).- Text Quality, Text Variety, and Parsing XML.- Clustering.- Classification.- Topic Modeling.- Appendix A: Variable Scope Example.- Appendix B: The LDA Buffet.- Appendix C: Code Repository.- Appendix D: R Resources.- Practice Exercise Solutions.- Index.