quanteda.textmodels: 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.

Package details

AuthorKenneth 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)
MaintainerKenneth Benoit <kbenoit@lse.ac.uk>
URL https://github.com/quanteda/quanteda.textmodels
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:

Try the quanteda.textmodels package in your browser

Any scripts or data that you put into this service are public.

quanteda.textmodels documentation built on March 26, 2020, 9:19 p.m.