plotResults

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

plotResults function

<dl><dt>path</dt>
<dd>path to store the plot</dd>
<dt>x</dt>
<dd>a vector of counts of successes; could be obtained from getResults()</dd>
<dt>n</dt>
<dd>a vector of counts of trials</dd>
<dt>taskname</dt>
<dd>the name of the task for labeling, e.g., Word Intrusion, Optimal Label.</dd>
<dt>...</dt>
<dd>additional arguments to be passed to plot function</dd></dl>

Arguments

Plot results — plotResults

<dl>

<dt>path</dt>
<dd>path to store the plot</dd>


<dt>x</dt>
<dd>a vector of counts of successes; could be obtained from getResults()</dd>


<dt>n</dt>
<dd>a vector of counts of trials</dd>


<dt>taskname</dt>
<dd>the name of the task for labeling, e.g., Word Intrusion, Optimal Label.</dd>


<dt>...</dt>
<dd>additional arguments to be passed to plot function</dd>

</dl>

plotResults: Plot results

Description

Usage

Value

Arguments

Details