keypostedtest

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

keypostedtest function

A list of two data frames. Similar to <code>recordtest</code>.
<dl>
<dt><code>data.frame1</code></dt>
<dd>A data frame of tasks with the <code>optcrt</code> indicating the 
machine predicted choice.</dd><dt><code>data.frame2</code></dt>
<dd>A data frame of tasks with randomized choices. 
Exactly the same with what would be sent online.</dd>
</dl>

Format

Example Answer Keys — keypostedtest

A list of two data frames. Similar to <code>recordtest</code>.
<dl>
<dt><code>data.frame1</code></dt>
<dd>A data frame of tasks with the <code>optcrt</code> indicating the 
machine predicted choice.</dd>

<dt><code>data.frame2</code></dt>
<dd>A data frame of tasks with randomized choices. 
Exactly the same with what would be sent online.</dd>


</dl>

keypostedtest: Example Answer Keys

Description

Usage

Arguments

Format