summary.textmodel_nb

summary method for textmodel_nb objects

internal

textmodel

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.

Kenneth Benoit

quanteda.textmodels

Scaling Models and Classifiers for Textual Data

Kohei Watanabe

Haiyan Wang

Patrick O. Perry

Benjamin Lauderdale

Johannes Gruber

William Lowe

Vikas Sindhwani

European Research Council 

summary.textmodel_nb function

<dl><dt>object</dt>
<dd>output from <code>textmodel_nb()</code></dd>
<dt>n</dt>
<dd>how many coefficients to print before truncating</dd>
<dt>...</dt>
<dd>additional arguments not used</dd></dl>

Arguments

summary method for textmodel_nb objects — summary.textmodel_nb

<dl>

<dt>object</dt>
<dd>output from <code>textmodel_nb()</code></dd>


<dt>n</dt>
<dd>how many coefficients to print before truncating</dd>


<dt>...</dt>
<dd>additional arguments not used</dd>

</dl>

summary method for textmodel_nb objects

summary.textmodel_nb: summary method for textmodel_nb objects

Description

Usage

Value

Arguments