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Text Mining with R: A Tidy Approach: 194 pages

Author : Julia Silge, David Robinson

Language : English

1) Book Description

This book is focused on practical software examples and data explorations. There are few equations, but a great deal of code. We especially focus on generating real insights from the literature, news, and social media that we analyze.

We don’t assume any previous knowledge of text mining. Professional linguists and text analysts will likely find our examples elementary, though we are confident they can build on the framework for their own analyses.

We do assume that the reader is at least slightly familiar with dplyr, ggplot2, and the %>% 'pipe' operator in R, and is interested in applying these tools to text data. For users who don’t have this background, we recommend books such as R for Data Science. We believe that with a basic background and interest in tidy data, even a user early in their R career can understand and apply our examples.

2) About Author

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.