allR4WSItasktest

Data frame of 20 example R4WSI0 Tasks, with 5 of them being gold-standard and 15 of them not.

datasets

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

allR4WSItasktest function

A data frame of 20 rows and 6 columns.<dl>
<dt><code>topic</code></dt>
<dd>Index of topics</dd><dt><code>id</code></dt>
<dd>Index of topics</dd><dt><code>doc</code></dt>
<dd>Example documents associated with each topic</dd><dt><code>opt1</code></dt>
<dd>Words set option 1</dd><dt><code>opt2</code></dt>
<dd>Words set option 2</dd><dt><code>opt3</code></dt>
<dd>Words set option 3</dd><dt><code>optcrt</code></dt>
<dd>Words set option 4, also the correct choice</dd>
</dl>

Format

Example R4WSI Tasks with Regular and Gold-Standard Tasks — allR4WSItasktest

A data frame of 20 rows and 6 columns.<dl>
<dt><code>topic</code></dt>
<dd>Index of topics</dd>

<dt><code>id</code></dt>
<dd>Index of topics</dd>

<dt><code>doc</code></dt>
<dd>Example documents associated with each topic</dd>

<dt><code>opt1</code></dt>
<dd>Words set option 1</dd>

<dt><code>opt2</code></dt>
<dd>Words set option 2</dd>

<dt><code>opt3</code></dt>
<dd>Words set option 3</dd>

<dt><code>optcrt</code></dt>
<dd>Words set option 4, also the correct choice</dd>


</dl>

allR4WSItasktest: Example R4WSI Tasks with Regular and Gold-Standard Tasks

Description

Usage

Arguments

Format