by_strata_TopicWord

Estimate subsetted topic-word distribution

Fits keyword assisted topic models (keyATM) using collapsed Gibbs samplers. The keyATM combines the latent dirichlet allocation (LDA) models with a small number of keywords selected by researchers in order to improve the interpretability and topic classification of the LDA. The keyATM can also incorporate covariates and directly model time trends. The keyATM is proposed in Eshima, Imai, and Sasaki (2024) <doi:10.1111/ajps.12779>.

Shusei Eshima

keyATM

Keyword Assisted Topic Models

Tomoya Sasaki

Kosuke Imai

Chung-hong Chan

Romain François

Martin Feldkircher

William Lowe

Seo-young Silvia Kim

by_strata_TopicWord function

<dl><dt>x</dt>
<dd>the output from a keyATM model (see <code>keyATM()</code>).</dd>
<dt>keyATM_docs</dt>
<dd>an object generated by <code>keyATM_read()</code>.</dd>
<dt>by</dt>
<dd>a vector whose length is the number of documents.</dd></dl>

Arguments

Estimate subsetted topic-word distribution — by_strata_TopicWord

<dl>

<dt>x</dt>
<dd>the output from a keyATM model (see <code>keyATM()</code>).</dd>


<dt>keyATM_docs</dt>
<dd>an object generated by <code>keyATM_read()</code>.</dd>


<dt>by</dt>
<dd>a vector whose length is the number of documents.</dd>

</dl>

by_strata_TopicWord: Estimate subsetted topic-word distribution

Description

Usage

Value

Arguments