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) <10.1017>,
'Wordscores' model, Perry and 'Benoit's' (2017) <1710.08963> class affinity scaling model,
and 'Slapin' and 'Proksch's' (2008) <10.1111> '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.10.1111>1710.08963>10.1017>