printTopWords

This is a generic function that prints the top words for each
document for low, average, and high levels of
sentiment-discourse

The Structural Topic and Sentiment-Discourse (STS) model allows researchers to estimate topic models with document-level metadata that determines both topic prevalence and sentiment-discourse. The sentiment-discourse is modeled as a document-level latent variable for each topic that modulates the word frequency within a topic. These latent topic sentiment-discourse variables are controlled by the document-level metadata. The STS model can be useful for regression analysis with text data in addition to topic modeling’s traditional use of descriptive analysis. The method was developed in Chen and Mankad (2024) <doi:10.1287/mnsc.2022.00261>.

Shawn Mankad

Estimation of the Structural Topic and Sentiment-Discourse Model
for Text Analysis

Li Chen

printTopWords function

<dl><dt>object</dt>
<dd>Model output from sts</dd>
<dt>n</dt>
<dd>number of words to print to console for each topic</dd>
<dt>lowerPercentile</dt>
<dd>Percentile to calculate a representative negative sentiment document.</dd>
<dt>upperPercentile</dt>
<dd>Percentile to calculate a representative positive sentiment document.</dd></dl>

Arguments

A Generic Function to Print Top Words — printTopWords

<dl>

<dt>object</dt>
<dd>Model output from sts</dd>


<dt>n</dt>
<dd>number of words to print to console for each topic</dd>


<dt>lowerPercentile</dt>
<dd>Percentile to calculate a representative negative sentiment document.</dd>


<dt>upperPercentile</dt>
<dd>Percentile to calculate a representative positive sentiment document.</dd>

</dl>

Last chance! 50% off unlimited learning

printTopWords: A Generic Function to Print Top Words

Description

Usage

Arguments

See Also