Appendix: Text analysis

Reading

• Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund, R for Data Science (2nd Edition): Chapters 14 and 15.
• Julia Silge and David Robinson, Text Mining with R: A Tidy Approach, (O’Reilly, 2017), https://www.tidytextmining.com.
  • Read the Preface and Chapter 1: The tidy text format and look through other chapters to see what you might be of particular interest to your concerns.
• Taylor Arnold, Nicolas Ballier, Paula Lissón, and Lauren Tilton, “Beyond Lexical Frequencies: Using R for Text Analysis in the Digital Humanities,” Language Resources and Evaluation 53, no. 4 (2019): 707–733, https://doi.org/10.1007/s10579-019-09456-6.

Assignment

• Look at the documentation for the text editor that you are using on regular expressions (regex) and/or advanced find and replace features.
  • The Chapter on Searching with Grep in the BBEdit Manual is particularly good.
• Browse Taylor Arnold, Courtney Rivard, and Lauren Tilton, Layered Lives: Rhetoric and Representation in the Southern Life History Project (Stanford University Press, 2022): https://doi.org/10.21627/2022ll.

Activities

• Discussion of the use of text analysis in History.
• Workshop: Regular expressions (regex)
• Workshop: Text analysis with R

Resources

Regular expressions (regex)

• Programming Historian - Doug Knox, Understanding Regular Expressions.
• stringr: Regular expressions
• Joshua Fjelstul, Regular expressions in R.

Text analysis R packages

• CRAN Task View: Natural Language Processing
• stringr
• quanteda
• cleanNLP

Text analysis

• Programming Historian - Taylor Arnold and Lauren Tilton, Basic Text Processing in R.
• Programming Historian - Jennifer Isasi, Sentiment Analysis with ‘syuzhet’ using R.
• Emil Hvitfeldt and Julia Silge, Supervised Machine Learning for Text Analysis in R, (CRC Press 2021), https://smltar.com.
  • This books is an updated but more advanced discussion of text analysis in R than Text Mining with R: A Tidy Approach.
• The Data Sitters Club