Text Mining in R: A Tidy Approach

January 13, 2017

(This article was first published on data science ish, and kindly contributed to R-bloggers)

I spoke on approaching text mining tasks using tidy data principles at rstudio::conf yesterday. I was so happy to have the opportunity to speak and the conference has been a great experience.

If you want to catch up on what has been going on at rstudio::conf, Karl Broman put together a GitHub repo of slides and Sharon Machlis has been live-blogging the conference at Computerworld. A highlight for me was Andrew Flowers’ talk on data journalism and storytelling; I don’t work in data journalism but I think I can apply almost everything he said to how I approach what I do.

My collaborator David Robinson and I made a very exciting announcement at the conference; we are publishing our book on text mining in R using tidy data principles with O’Reilly!


The book will continue to be available online; we are working on getting the content finished and you can expect to see the published product from O’Reilly the middle of this year. I was really pleased to have great discussions with people at rstudio::conf about how they are analyzing text and approaching natural language processing, and it was a pleasure to just connect with people in the R community in general.

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