summary.textmodel_wordfish

summary method for textmodel_wordfish

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_wordfish function

<dl><dt>object</dt>
<dd>a textmodel_wordfish object</dd>
<dt>n</dt>
<dd>maximum number of features to print in summary</dd>
<dt>...</dt>
<dd>unused</dd></dl>

Arguments

summary method for textmodel_wordfish — summary.textmodel_wordfish

<dl>

<dt>object</dt>
<dd>a textmodel_wordfish object</dd>


<dt>n</dt>
<dd>maximum number of features to print in summary</dd>


<dt>...</dt>
<dd>unused</dd>

</dl>

summary method for textmodel_wordfish

summary.textmodel_wordfish: summary method for textmodel_wordfish

Description

Usage

Value

Arguments