Text Mining with R: A Tidy Approach

A large portion of the data available today is unstructured and text-heavy, making it difficult for analysts to apply their usual data preparation and visualization tools. In this practical book, you will explore text mining techniques with tidytext, a package developed by authors Julia Silge and David Robinson using the tidy principles behind R packages like ggraph and dplyr. You will learn how tidytext and other tidy tools in R can make text analysis easier and more effective. The authors demonstrate how treating text as a data frame allows you to manipulate, summarize, and visualize the properties of text. You will also learn how to integrate natural language processing (NLP) into effective workflows. Practical code examples and data analyses will help you gain real insights from literature, news, and social media.

Discover how to apply the tidy text format to NLP, use sentiment analysis to capture the emotional content of texts, identify key terms in a document with frequency measurements, explore relationships and connections between words using the ggraph and widyr packages, convert between R's tidy and non-tidy text formats, use topic modeling to classify document collections into natural groups, and examine case studies that compare Twitter archives, analyze NASA metadata, and investigate thousands of Usenet messages.