summarize_topics

Summarizes topics in a model. Called by <code>tidylda</code>
 and <code>refit.tidylda</code> and used to augment
 <code>print.tidylda</code>.

internal

Implements an algorithm for Latent Dirichlet
Allocation (LDA), Blei et at. (2003) <https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf>,
using style conventions from the 'tidyverse',
Wickham et al. (2019)<doi:10.21105/joss.01686>,
and 'tidymodels', Kuhn et al.<https://tidymodels.github.io/model-implementation-principles/>.
Fitting is done via collapsed Gibbs sampling.
Also implements several novel features for LDA such as guided models and
transfer learning.

Thomas W Jones

tidylda

Latent Dirichlet Allocation Using 'tidyverse' Conventions

Tommy Jones

Brendan Knapp

Barum Park

summarize_topics function

<dl><dt>theta</dt>
<dd>numeric matrix whose rows represent P(topic|document)</dd>
<dt>beta</dt>
<dd>numeric matrix whose rows represent P(token|topic)</dd>
<dt>dtm</dt>
<dd>a document term matrix or term co-occurrence matrix of class <code>dgCMatrix</code>.</dd></dl>

Arguments

Summarize a topic model consistently across methods/functions — summarize_topics

<dl>

<dt>theta</dt>
<dd>numeric matrix whose rows represent P(topic|document)</dd>


<dt>beta</dt>
<dd>numeric matrix whose rows represent P(token|topic)</dd>


<dt>dtm</dt>
<dd>a document term matrix or term co-occurrence matrix of class <code>dgCMatrix</code>.</dd>

</dl>

summarize_topics: Summarize a topic model consistently across methods/functions

Description

Usage

Value

Arguments