Predict continuous valued outputs associated with text documents. The input corpus of text documents is transformed into a document-term matrix (DTM) and then a regularized linear regression is fit that uses this matrix as predictors to predict the continuous valued output. The corpus's terms, coefficients for all terms and an estimate of the model's predictive power are returned in a list.
| Version: | 0.1-3 |
| Depends: | tm, Matrix, glmnet, plyr |
| Suggests: | testthat |
| Published: | 2012-05-13 |
| Author: | John Myles White |
| Maintainer: | John Myles White <jmw at johnmyleswhite.com> |
| License: | Artistic-2.0 |
| NeedsCompilation: | no |
| In views: | NaturalLanguageProcessing |
| CRAN checks: | TextRegression results |
| Package source: | TextRegression_0.1-3.tar.gz |
| MacOS X binary: | TextRegression_0.1-3.tgz |
| Windows binary: | TextRegression_0.1-3.zip |
| Reference manual: | TextRegression.pdf |
| News/ChangeLog: | ChangeLog |
| Old sources: | TextRegression archive |