A fast, flexible, and comprehensive framework for
quantitative text analysis in R.  Provides functionality for corpus management,
creating and manipulating tokens and n-grams, exploring keywords in context,
forming and manipulating sparse matrices
of documents by features and feature co-occurrences, analyzing keywords, computing feature similarities and
distances, applying content dictionaries, applying supervised and unsupervised machine learning,
visually representing text and text analyses, and more.

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) <doi:10.1017/S0003055403000698>,
'Wordscores' model, Perry and 'Benoit's' (2017) <arXiv:1710.08963> class affinity scaling model,
and 'Slapin' and 'Proksch's' (2008) <doi:10.1111/j.1540-5907.2008.00338.x> '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.
c(
person("Kenneth", "Benoit", email = "kbenoit@lse.ac.uk", role = c("cre", "aut", "cph"), comment = c(ORCID = "0000-0002-0797-564X")),
person("Kohei", "Watanabe", email = "watanabe.kohei@gmail.com", role = "aut", comment = c(ORCID = "0000-0001-6519-5265")),
person("Haiyan", "Wang", email = "whyinsa@yahoo.com", role = "aut", comment = c(ORCID = "0000-0003-4992-4311")),
person("Stefan", "Müller", email = "mullers@tcd.ie", role = "aut", comment = c(ORCID = "0000-0002-6315-4125")),
person("Patrick O.", "Perry", email = "patperry@gmail.com", role = "aut", comment = c(ORCID = "0000-0001-7460-127X")),
person("Benjamin", "Lauderdale", email = "b.e.lauderdale@lse.ac.uk", role = "aut", comment = c(ORCID = "0000-0003-3090-0969")),
person("William", "Lowe", email = "wlowe@princeton.edu", role = "aut", comment = c(ORCID = "0000-0002-1549-6163")),
person("European Research Council", role = "fnd", comment = "ERC-2011-StG 283794-QUANTESS")
)
ggplot2, LiblineaR, Matrix (>= 1.2), quanteda (>= 2.0),
RSpectra, Rcpp (>= 0.12.12), RcppParallel, RSSL, SparseM,
stringi
ca, covr, knitr, lsa, spelling, RColorBrewer, testthat
Rcpp, RcppParallel, RcppArmadillo (>= 0.7.600.1.0), quanteda
'RcppExports.R' 'quanteda.textmodels-package.R' 'aaa.R'
'data-documentation.R' 'data.R' 'textmodel-methods.R'
'textmodel_affinity.R' 'textmodel_ca.R' 'textmodel_lsa.R'
'textmodel_nb.R' 'textmodel_svm.R' 'textmodel_svmlin.R'
'textmodel_wordfish.R' 'textmodel_wordscores.R'
'textplot_influence.R' 'textplot_scale1d.R'
Kenneth Benoit [cre, aut, cph]
(<https://orcid.org/0000-0002-0797-564X>),
Kohei Watanabe [aut] (<https://orcid.org/0000-0001-6519-5265>),
Haiyan Wang [aut] (<https://orcid.org/0000-0003-4992-4311>),
Stefan Müller [aut] (<https://orcid.org/0000-0002-6315-4125>),
Patrick O. Perry [aut] (<https://orcid.org/0000-0001-7460-127X>),
Benjamin Lauderdale [aut] (<https://orcid.org/0000-0003-3090-0969>),
William Lowe [aut] (<https://orcid.org/0000-0002-1549-6163>),
European Research Council [fnd] (ERC-2011-StG 283794-QUANTESS)