evalResults

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

evalResults function

<dl><dt>results</dt>
<dd>results of human choice; outputs from getResults()</dd>
<dt>key</dt>
<dd>the local task record; outputs form recordTasks()</dd>
<dt>type</dt>
<dd>Task structures to be specified. Must be one of "WI" (word intrusion),
"T8WSI" (top 8 word set intrusion), "R4WSI" (random 4 word set intrusion),
"LI" (Label Intrusion), and "OL" (Optimal Label)</dd></dl>

Arguments

Evaluate results — evalResults

<dl>

<dt>results</dt>
<dd>results of human choice; outputs from getResults()</dd>


<dt>key</dt>
<dd>the local task record; outputs form recordTasks()</dd>


<dt>type</dt>
<dd>Task structures to be specified. Must be one of "WI" (word intrusion),
"T8WSI" (top 8 word set intrusion), "R4WSI" (random 4 word set intrusion),
"LI" (Label Intrusion), and "OL" (Optimal Label)</dd>

</dl>

evalResults: Evaluate results

Description

Usage

Value

Arguments

Details