The R Journal: article published in 2013, volume 5:1

RTextTools: A Supervised Learning Package for Text Classification
Timothy P. Jurka, Loren Collingwood, Amber E. Boydstun, Emiliano Grossman and Wouter van Atteveldt , The R Journal (2013) 5:1, pages 6-12.

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.

Received: 2011-08-19; online 2013-06-03
CRAN packages: RTextTools, glmnet, maxent, e1071, tm, ipred, caTools, randomForest, nnet, tree , CRAN Task Views implied by cited CRAN packages: MachineLearning, Environmetrics, NaturalLanguageProcessing, Survival, Cluster, Distributions, Econometrics, HighPerformanceComputing, Multivariate, Psychometrics, SocialSciences


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@article{RJ-2013-001,
  author = {Timothy P. Jurka and Loren Collingwood and Amber E. Boydstun
          and Emiliano Grossman and Wouter van Atteveldt},
  title = {{RTextTools: A Supervised Learning Package for Text
          Classification}},
  year = {2013},
  journal = {{The R Journal}},
  url = {https://journal.r-project.org/archive/2013/RJ-2013-001/index.html},
  pages = {6--12},
  volume = {5},
  number = {1}
}