Text Mining with R A Tidy Approach

Tackle a variety of tasks in natural language processing by learning how to use the R language and tidy data principles. This practical guide provides examples and resources to help you get up to speed with dplyr, broom, ggplot2, and other tidy tools from the R ecosystem. You’ll discover how tidy data principles can make text mining easier, more effective, and consistent by employing tools already in wide use.

Text Mining with R shows you how to manipulate, summarize, and visualize the characteristics of text, sentiment analysis, tf-idf, and topic modeling. Along with tidy data methods, you’ll also examine several beginning-to-end tidy text analyses on data sources from Twitter to NASA datasets. These analyses bring together multiple text mining approaches covered in the book.

- Chapter 1 - The tidy text format
- Chapter 2 - Sentiment analysis with tidy data
- Chapter 3 - Analyzing word and document frequency: tf-idf
- Chapter 4 - Relationships between words: n-grams and correlations
- Chapter 5 - Converting to and from non-tidy formats
- Chapter 6 - Topic modeling
- Chapter 7 - Case study: comparing Twitter archives
- Chapter 8 - Case study: mining NASA metadata
- Chapter 9 - Case study: analyzing usenet text

Julia Silge is a data scientist at Stack Overflow; her work involves analyzing complex datasets and communicating about technical topics with diverse audiences.

David Robinson is a data scientist at Stack Overflow. He has a PhD in Quantitative and Computational Biology from Princeton University, where he worked with Professor John Storey on genomic analysis.