Text Mining with R: A Tidy Approach

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. 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. 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. 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 NLPUse sentiment analysis to mine the emotional content of textIdentify a document’s most important terms with frequency measurementsExplore relationships and connections between words with the ggraph and widyr packagesConvert back and forth between R’s tidy and non-tidy text formatsUse topic modeling to classify document collections into natural groupsExamine case studies that compare Twitter archives, dig into NASA metadata, and analyze thousands of Usenet messages

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

An excellent way to start or develop text analysis skills Omar W This book was enormously helpful for a new research project that required significant text analysis. The writing and sample code are very clear and easy to follow. If you've never done text analysis and just want to get started, this is an excellent way to begin. If you have worked with other text analysis methods or packages, I still recommend the book highly as the tidytext / tidyverse approach is sufficiently different t. "Five Stars" according to Mari Marcondes. Great book: well explained and easy to follow examples. The tidytext methodology is simple to use and elegant.

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