Statistics “Sunday”: More Sentiment Analysis Resources

[This article was first published on Deeply Trivial, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

I’ve just returned from a business trip – lots of long days, working sometimes from 8 am to 9 pm or 10 pm. I didn’t get a chance to write this week’s Statistics Sunday post, in part because I wasn’t entirely certain what to write about. But as I started digging into sentiment analysis tools for a fun project I’m working on – and will hopefully post about soon – I found a few things I wanted to share.

The tidytext package in R is great for tokenizing text and running sentiment analysis with 4 dictionaries: Afinn, Bing, Loughran, and NRC. During some web searches for additional tricks to use with these tools, I found another R package: syuzhet, which includes Afinn, Bing, and NRC, as well as Syuzhet, developed in the Nebraska Literacy Lab, and a method to access the powerful Stanford Natural Language Processing and sentiment analysis software, which can predict sentiment through deep learning methods.

I plan to keep using the tidytext package for much of this analysis, but will probably draw upon the syuzhet package for some of the sentiment analysis, especially to use the Stanford methods. And there are still some big changes on the horizon for Deeply Trivial, including more videos and a new look!

To leave a comment for the author, please follow the link and comment on their blog: Deeply Trivial. offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)