About this item
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.
About the Author
I enjoy making beautiful charts, the statistical programming language R, and the work of Jane Austen. I have a PhD in astrophysics and worked in academia and ed tech before moving in data science. My work involves analyzing and modeling complex data sets while communicating about technical topics with diverse audiences. I work at Stack Overflow and live in Salt Lake City, UT, with my husband, three children, and a handful of backyard chickens.
