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, Perry and 'Benoit's' (2017) class affinity scaling model, and 'Slapin' and 'Proksch's' (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.


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Reference manual

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install.packages("quanteda.textmodels")

0.9.1 by Kenneth Benoit, 6 months ago


https://github.com/quanteda/quanteda.textmodels


Browse source code at https://github.com/cran/quanteda.textmodels


Authors: Kenneth Benoit [cre, aut, cph] , Kohei Watanabe [aut] , Haiyan Wang [aut] , Stefan Müller [aut] , Patrick O. Perry [aut] , Benjamin Lauderdale [aut] , William Lowe [aut] , European Research Council [fnd] (ERC-2011-StG 283794-QUANTESS)


Documentation:   PDF Manual  


GPL-3 license


Imports ggplot2, LiblineaR, Matrix, quanteda, RSpectra, Rcpp, RcppParallel, RSSL, SparseM, stringi

Depends on methods

Suggests ca, covr, fastNaiveBayes, knitr, lsa, microbenchmark, naivebayes, spelling, RColorBrewer, testthat, rmarkdown

Linking to Rcpp, RcppParallel, RcppArmadillo, quanteda

System requirements: C++11


Depended on by LSX.

Suggested by explor, quanteda, rainette, seededlda.


See at CRAN