record

Reform tasks to facilitate sending to Mturk

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

record function

<dl><dt>type</dt>
<dd>(character) one of WI, T8WSI, R4WSI</dd>
<dt>tasks</dt>
<dd>(data.frame) outputs from validateTopic(), validateLabel(), or mixGold() if users mix in gold-standard HITs</dd>
<dt>path</dt>
<dd>(character) path to record the tasks (with meta-information)</dd></dl>

Arguments

Reform tasks to facilitate sending to Mturk — record

<dl>

<dt>type</dt>
<dd>(character) one of WI, T8WSI, R4WSI</dd>


<dt>tasks</dt>
<dd>(data.frame) outputs from validateTopic(), validateLabel(), or mixGold() if users mix in gold-standard HITs</dd>


<dt>path</dt>
<dd>(character) path to record the tasks (with meta-information)</dd>

</dl>

record: Reform tasks to facilitate sending to Mturk

Description

Usage

Value

Arguments

Details