Sentiment analysis using tidy data principles at DataCamp

NOTE: Read more here about why I no longer recommend taking my courses at DataCamp. I’ve been developing a course at DataCamp over the past several months, and I am happy to announce that it is now launched! The course is Sentiment Analysis in R: the Tidy Way and I am excited that it is now available for you to explore and learn from. This course focuses on digging into the emotional and opinion content of text using sentiment analysis, and it does this from the specific perspective of using tools built for handling tidy data.

Understanding gender roles in movies with text mining

I have a new visual essay up at The Pudding today, using text mining to explore how women are portrayed in film. The R code behind this analysis in publicly available on GitHub. I was so glad to work with the talented Russell Goldenberg and Amber Thomas on this project, and many thanks to Matt Daniels for inviting me to contribute to The Pudding. I’ve been a big fan of their work for a long time!

Seeking guidance in choosing and evaluating R packages

At useR!2017 in Brussels last month, I contributed to an organized session focused on navigating the 11,000+ packages on CRAN. My collaborators on this session and I recently put together an overall summary of the session and our goals, and now I’d like to talk more about the specific issue of learning about R packages and deciding which ones to use. John and Spencer will write more soon about the two other issues of our focus:

Navigating the R Package Universe

Earlier this month, I, along with John Nash, Spencer Graves, and Ludovic Vannoorenberghe, organized a session at useR!2017 focused on discovering, learning about, and evaluating R packages. You can check out the recording of the session. There are more than 11,000 packages on CRAN, and R users must approach this abundance of packages with effective strategies to find what they need and choose which packages to invest time in learning how to use.

Text Mining of Stack Overflow Questions

Note: Cross-posted with the Stack Overflow blog. This week, my fellow Stack Overflow data scientist David Robinson and I are happy to announce the publication of our book Text Mining with R with O’Reilly. We are so excited to see this project out in the world, and so relieved to finally be finished with it! Text data is being generated all the time around us, in healthcare, finance, tech, and beyond; text mining allows us to transform that unstructured text data into real insight that can increase understanding and inform decision-making.