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Bibliographic Details

Title: Text Mining with R: A Tidy Approach
Publisher: O'Reilly Media
Publication Date: 2017
Language: English
ISBN 10: 1491981652
ISBN 13: 9781491981658
Binding: Soft cover
Condition: acceptable

About this title

Synopsis

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

About the Authors

Julia Silge is a data scientist at Stack Overflow; her work involves analyzing complex datasets and communicating about technical topics with diverse audiences. She has a PhD in astrophysics and loves Jane Austen and making beautiful charts. Julia worked in academia and ed tech before moving into data science and discovering the statistical programming language R.

David Robinson is a data scientist at Stack Overflow with a PhD in Quantitative and Computational Biology from Princeton University. He enjoys developing open source R packages, including broom, gganimate, fuzzyjoin and widyr, as well as blogging about statistics, R, and text mining on his blog, Variance Explained.

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