Scaling Models and Classifiers for Textual Data

Scaling models and classifiers for sparse matrix objects representing textual data in the form of a document-feature matrix. Includes original implementations of 'Laver', 'Benoit', and Garry's (2003) , 'Wordscores' model, the Perry and 'Benoit' (2017) class affinity scaling model, and the 'Slapin' and 'Proksch' (2008) 'wordfish' model, as well as methods for correspondence analysis, latent semantic analysis, and fast Naive Bayes and linear 'SVMs' specially designed for sparse textual data.

quanteda.textmodels

About

An R package adding text scaling models and classifiers for quanteda. Prior to quanteda v2, many of these were part of that package. Early development was supported by the European Research Council grant ERC-2011-StG 283794-QUANTESS.

For more details, see https://quanteda.io.

How to Install

You can install it via the normal way from CRAN, using your R GUI or

install.packages("quanteda.textmodels") 

Or for the latest development version:

# devtools package required to install quanteda from Github 
remotes::install_github("quanteda/quanteda.textmodels") 

Because this compiles some C++ and Fortran source code, you will need to have installed the appropriate compilers. On Windows platform, this means the Rtools software available from CRAN, or the macOS tools from macOS tools, including namely the Clang 6.x compiler and the GNU Fortran compiler (as quanteda requires gfortran to build). If you are still getting errors related to gfortran, follow the fixes here.

How to cite

Benoit, Kenneth, Kohei Watanabe, Haiyan Wang, Paul Nulty, Adam Obeng, Stefan Müller, and Akitaka Matsuo. (2018) “quanteda: An R package for the quantitative analysis of textual data”. Journal of Open Source Software. 3(30), 774. https://doi.org/10.21105/joss.00774.

For a BibTeX entry, use the output from citation(package = "quanteda").

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Contributing

Contributions in the form of feedback, comments, code, and bug reports are most welcome. How to contribute:

• Fork the source code, modify, and issue a pull request through the project GitHub page. See our Contributor Code of Conduct and the all-important quanteda Style Guide.
• Issues, bug reports, and wish lists: File a GitHub issue.
• Usage questions: Submit a question on the quanteda channel on StackOverflow.
• Contact the maintainer by email.