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.

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.