summary.STS

print.STS

Function to report on the contents of STS objects

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

summary.STS function

<dl><dt>object</dt>
<dd>An STS object.</dd>
<dt>...</dt>
<dd>Additional arguments affecting the summary</dd></dl>

Arguments

Summary Function for the STS objects — summary.STS

<dl>

<dt>object</dt>
<dd>An STS object.</dd>


<dt>...</dt>
<dd>Additional arguments affecting the summary</dd>

</dl>

summary.STS: Summary Function for the STS objects

Description

Usage

Arguments

Details