Chapter 8 Suggestions for Further Reading and Acknowledgements

8.1 Books

8.1.1 Natural Language Processing with R

• Text Analysis with R by Matthew L. Jockers and Rosamond Thalken

• Text Mining with R: A Tidy Approach by Julia Silge and David Robinson

• Supervised Machine Learning for Text Analysis in R by Emil Hvitfeldt and Julia Silge

8.1.2 Other Books on Natural Language Processing

• Practical Natural Language Processing: A Comprehensive Guide to Building Real-World NLP Systems by Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta and Harshit Surana

• The Bestseller Code: Anatomy of the Blockbuster Novel by Jodie Archer and Matthew L. Jockers

• Macroanalysis: Digital Methods and Literary History by Matthew L. Jockers

• Narrative Economics: How Stories Go Viral and Drive Major Economic Events by Robert J. Shiller

• The Secret Life of Pronouns: What Our Words Say About Us by James W. Pennebaker

8.1.3 R Markdown

• R Markdown Cookbook by Yihui Xie, Christophe Dervieux and Emily Riederer

• bookdown: Authoring Books and Technical Documents with R Markdown by Yihui Xie

• R Markdown The Definitive Guide by Yihui Xie, J.J. Allaire and Garrett Grolemund

8.2 Online Tutorials

• Michael Toth has an excellent primer on sentiment analysis of Berkshire Letters on his website.

• Len Kiefer has some fantastic R tutorials on his website. Text mining the 2020 Fed Beige Book and Beige-ian Statistics were both extremely helpful.