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.


Reference manual

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0.9.4 by Kenneth Benoit, 10 months ago

Browse source code at

Authors: Kenneth Benoit [cre, aut, cph] , Kohei Watanabe [aut] , Haiyan Wang [aut] , Patrick O. Perry [aut] , Benjamin Lauderdale [aut] , Johannes Gruber [aut] , William Lowe [aut] , Vikas Sindhwani [cph] (authored svmlin C++ source code) , European Research Council [fnd] (ERC-2011-StG 283794-QUANTESS)

Documentation:   PDF Manual  

GPL-3 license

Imports glmnet, LiblineaR, Matrix, quanteda, RSpectra, Rcpp, RcppParallel, SparseM, stringi

Depends on methods

Suggests ca, covr, fastNaiveBayes, knitr, lsa, microbenchmark, naivebayes, quanteda.textplots, spelling, testthat, rmarkdown

Linking to Rcpp, RcppParallel, RcppArmadillo, quanteda

System requirements: C++11

Suggested by explor, oolong, quanteda, quanteda.textplots, rainette, seededlda.

See at CRAN