combMass

Combine the mass of words with the same root

By creating crowd-sourcing tasks that can be easily posted and results retrieved using Amazon's Mechanical Turk (MTurk) API, researchers can use this solution to validate the quality of topics obtained from unsupervised or semi-supervised learning methods, and the relevance of topic labels assigned. This helps ensure that the topic modeling results are accurate and useful for research purposes. See Ying and others (2022) <doi:10.1101/2023.05.02.538599>. For more information, please visit <https://github.com/Triads-Developer/Topic_Model_Validation>.

Luwei Ying

validateIt

Validating Topic Coherence and Topic Labels

Jacob Montgomery

Brandon Stewart

combMass function

<dl><dt>mod</dt>
<dd>Fitted structural topic models.</dd>
<dt>vocab</dt>
<dd>A character vector specifying the words in the corpus. Usually, it
can be found in topic model output.</dd>
<dt>beta</dt>
<dd>A matrix of word probabilities for each topic. Each row represents a
topic and each column represents a word. Note this should not be in the logged form.</dd></dl>

Arguments

Combine the mass of words with the same root — combMass

<dl>

<dt>mod</dt>
<dd>Fitted structural topic models.</dd>


<dt>vocab</dt>
<dd>A character vector specifying the words in the corpus. Usually, it
can be found in topic model output.</dd>


<dt>beta</dt>
<dd>A matrix of word probabilities for each topic. Each row represents a
topic and each column represents a word. Note this should not be in the logged form.</dd>

</dl>

combMass: Combine the mass of words with the same root

Description

Usage

Value

Arguments

Details