Package: quanteda.textmodels 0.9.10

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, the Perry and 'Benoit' (2017) <doi:10.48550/arXiv.1710.08963> class affinity scaling model, and the 'Slapin' and 'Proksch' (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.

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], European Research Council [fnd]