Julia Silge

David Robinson

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. Get real-world examples for implementing text mining using tidy R package Understand natural language processing concepts like sentiment analysis, tf-idf, and topic modeling Learn how to analyze unstructured, text-heavy data using R language and ecosystem

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