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
