word_in_doc

Checking if a word is in a document

internal

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

word_in_doc function

<dl><dt>doc</dt>
<dd>a vector</dd>
<dt>word</dt>
<dd>a word to check</dd></dl>

Arguments

Checking if a word is in a document — word_in_doc

<dl>

<dt>doc</dt>
<dd>a vector</dd>


<dt>word</dt>
<dd>a word to check</dd>

</dl>

Checking if a word is in a document

word_in_doc: Checking if a word is in a document

Description

Usage

Value

Arguments