terms

<code><a href="/link/terms()?package=seededlda&version=1.4.3" data-mini-rdoc="seededlda::terms()">terms()</a></code> returns the most likely terms for topics based on the <code>phi</code> parameter.

Seeded Sequential LDA can classify sentences of texts into pre-define topics with a small number of seed words (Watanabe & Baturo, 2023) <doi:10.1177/08944393231178605>.
Implements Seeded LDA (Lu et al., 2010) <doi:10.1109/ICDMW.2011.125> and Sequential LDA (Du et al., 2012) <doi:10.1007/s10115-011-0425-1> with the distributed LDA algorithm (Newman, et al., 2009) for parallel computing.

Kohei Watanabe

seededlda

Seeded Sequential LDA for Topic Modeling

Phan Xuan-Hieu

terms function

<dl><dt>x</dt>
<dd>a LDA model fitted by <code>textmodel_seededlda()</code> or <code>textmodel_lda()</code>.</dd>
<dt>n</dt>
<dd>the number of terms to be extracted.</dd></dl>

Arguments

<code><a href='https://rdrr.io/r/stats/terms.html'>terms()</a></code> returns the most likely terms for topics based on the <code>phi</code> parameter.

Extract most likely terms — terms

<dl>

<dt>x</dt>
<dd>a LDA model fitted by <code>textmodel_seededlda()</code> or <code>textmodel_lda()</code>.</dd>


<dt>n</dt>
<dd>the number of terms to be extracted.</dd>

</dl>

terms: Extract most likely terms

Description

Usage

Value

Arguments

Details