Much of the data available today is unstructured and text-heavy, making
it challenging for analysts to apply their usual data wrangling and
visualization tools. With this practical book, you'll explore
text-mining techniques with tidytext, a package that authors Julia Silge
and David Robinson developed using the tidy principles behind R packages
like ggraph and dplyr. You'll learn how tidytext and other tidy
tools in R can make text analysis easier and more effective.
The authors demonstrate how treating text as data frames enables you to
manipulate, summarize, and visualize characteristics of text. You'll
also learn how to integrate natural language processing (NLP) into
effective workflows. Practical code examples and data explorations will
help you generate real insights from literature, news, and social media.
- Learn how to apply the tidy text format to NLP
- Use sentiment analysis to mine the emotional content of text
- Identify a document's most important terms with frequency measurements
- Explore relationships and connections between words with the ggraph
and widyr packages
- Convert back and forth between R's tidy and non-tidy text formats
- Use topic modeling to classify document collections into natural
groups
- Examine case studies that compare Twitter archives, dig into NASA
metadata, and analyze thousands of Usenet messages
