keyATM_fit_LDA

Run the Collapsed Gibbs sampler for weighted LDA

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

keyATM_fit_LDA function

<dl><dt>model</dt>
<dd>A initialized model</dd>
<dt>resume</dt>
<dd>resume or not</dd></dl>

Arguments

Run the Collapsed Gibbs sampler for weighted LDA — keyATM_fit_LDA

<dl>

<dt>model</dt>
<dd>A initialized model</dd>


<dt>resume</dt>
<dd>resume or not</dd>

</dl>

Run the Collapsed Gibbs sampler for weighted LDA

keyATM_fit_LDA: Run the Collapsed Gibbs sampler for weighted LDA

Description

Usage

Arguments