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