RTextTools is a free, open source machine learning package for automatic text classification that makes it simple for both novice and advanced users to get started with supervised learning. The package includes nine algorithms for ensemble classification (svm, slda, boosting, bagging, random forests, glmnet, decision trees, neural networks, maximum entropy), comprehensive analytics, and thorough documentation.

The beta release was unveiled at the The 4th Annual Conference of the Comparative Policy Agendas Project on June 24, 2011 in Catania, Italy. The full release is available on the installation page.

Since its release, RTextTools has been used by researchers at universities across the world: Columbia, Cornell, Dartmouth, Harvard, Johns Hopkins University, MIT, NYU, Northwestern, Oxford, Princeton, Sciences Po Paris, Stanford, UC Berkeley, UC Davis, UC Los Angeles, UC San Diego, University of Chicago, University of Michigan, University of North Carolina-Chapel Hill, University of Tokyo, University of Warsaw, University of Washington, Vanderbilt, Washington University in St. Louis, Yale, and many others.

The RTextTools repository is available via GitHub, and the help mailing list is on Google Groups.

We would like to sincerely thank those who financed the RTextTools project-- Professor Amber Boydstun at University of California at Davis, Professor Sylvain Brouard at Sciences Po Bordeaux, Professor Emiliano Grossman at Sciences Po Paris, and Professor Stefaan Walgrave at University of Antwerp.

Additionally, we'd like to acknowledge our beta testers for their time and valuable feedback- Anna Maria Palau Roque, Jon Moody, Professor John D. Wilkerson, and Paul Wolfgang.