findTopic

Find topics that contain user specified words.

The Structural Topic Model (STM) allows researchers
to estimate topic models with document-level covariates.
The package also includes tools for model selection, visualization,
and estimation of topic-covariate regressions. Methods developed in
Roberts et. al. (2014) <doi:10.1111/ajps.12103> and
Roberts et. al. (2016) <doi:10.1080/01621459.2016.1141684>. Vignette
is Roberts et. al. (2019) <doi:10.18637/jss.v091.i02>.

Brandon Stewart

Estimation of the Structural Topic Model

Margaret Roberts

Dustin Tingley

Kenneth Benoit

findTopic function

<dl><dt>x</dt>
<dd>The STM model object to be searched. May also be the output from
sageLabels.</dd>
<dt>list</dt>
<dd>Character vector containing words to be searched.</dd>
<dt>n</dt>
<dd>Number of words to consider</dd>
<dt>type</dt>
<dd>Type of words to be searched.</dd>
<dt>verbose</dt>
<dd>A logical indicating whether details should be printed to the
screen.</dd></dl>

Arguments

Find topics that contain user specified words. — findTopic

<dl>

<dt>x</dt>
<dd>The STM model object to be searched. May also be the output from
sageLabels.</dd>


<dt>list</dt>
<dd>Character vector containing words to be searched.</dd>


<dt>n</dt>
<dd>Number of words to consider</dd>


<dt>type</dt>
<dd>Type of words to be searched.</dd>


<dt>verbose</dt>
<dd>A logical indicating whether details should be printed to the
screen.</dd>

</dl>

findTopic: Find topics that contain user specified words.

Description

Usage

Arguments

See Also

Examples