textmodels: quanteda.textmodels: Scaling Models and Classifiers for...

In quanteda.textmodels: Scaling Models and Classifiers for Textual Data

quanteda.textmodels: Scaling Models and Classifiers for Textual Data

Description

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) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1017/S0003055403000698")}, 'Wordscores' model, the Perry and 'Benoit' (2017) arXiv:1710.08963 class affinity scaling model, and the 'Slapin' and 'Proksch' (2008) \Sexpr[results=rd]{tools:::Rd_expr_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.

Author(s)

Maintainer: Kenneth Benoit [e-mail] (ORCID) [copyright holder]

Authors:

• Kohei Watanabe [e-mail] (ORCID)

• Haiyan Wang [e-mail] (ORCID)

• Patrick O. Perry [e-mail] (ORCID)

• Benjamin Lauderdale [e-mail] (ORCID)

• Johannes Gruber [e-mail] (ORCID)

• William Lowe [e-mail] (ORCID)

Other contributors:

• Vikas Sindhwani [e-mail] (authored svmlin C++ source code) [copyright holder]

• European Research Council (ERC-2011-StG 283794-QUANTESS) [funder]

See Also

Useful links:

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

quanteda.textmodels documentation built on March 31, 2023, 8:09 p.m.