RTextTools: A Supervised Learning Package for Text Classification

Authors

Timothy P. Jurka, University of California, Davis
Loren Collingwood, University of California, Riverside
Amber E. Boydstun, University of California, Davis
Emiliano Grossman, Sciences Po /CEE
Wouter van Atteveldt, Vrije Universiteit

Date of this Version

6-2013

Document Type

Article

Citation

The R Journal (June 2013) 5(1); Editor: Hadley Wickham

Comments

Copyright 2013, The R Foundation. Open access material. License: CC BY 3.0 Unported

Abstract

Social scientists have long hand-labeled texts to create datasets useful for studying topics from congressional policymaking to media reporting. Many social scientists have begun to incorporate machine learning into their toolkits. RTextTools was designed to make machine learning accessible by providing a start-to-finish product in less than 10 steps. After installing RTextTools, the initial step is to generate a document term matrix. Second, a container object is created, which holds all the objects needed for further analysis. Third, users can use up to nine algorithms to train their data. Fourth, the data are classified. Fifth, the classification is summarized. Sixth, functions are available for performance evaluation. Seventh, ensemble agreement is conducted. Eighth, users can cross-validate their data. Finally, users write their data to a spreadsheet, allowing for further manual coding if required.