Amazon.com: Text Mining with R: A Tidy Approach eBook: Julia Silge, David Robinson: Kindle Store

If you work in analytics or data science, like we do, you are familiar with the fact that data is being generated all the time at ever faster rates. Analysts are often trained to handle tabular or rectangular data that is mostly numeric, but much of the data proliferating today is unstructured and text-heavy. Many of us who work in analytical fields are not trained in even simple interpretation of natural language. We developed the tidytext (Silge and Robinson 2016) R package because we were familiar with many methods for data wrangling and visualization, but couldn't easily apply these same methods to text. We found that using tidy data principles can make many text mining tasks easier, more effective, and consistent with tools already in wide use.