findRepresentativeDocs

This is a generic function that extracts documents with the
highest prevalence for a given topic.

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

findRepresentativeDocs function

<dl><dt>object</dt>
<dd>Model output from sts</dd>
<dt>corpus_text</dt>
<dd>vector of text documents, usually contained in the output of prepDocuments</dd>
<dt>topic</dt>
<dd>a single topic number</dd>
<dt>n</dt>
<dd>number of documents to extract</dd></dl>

Arguments

A Generic Function to Identify Documents that Load Heavily on a Topic — findRepresentativeDocs

<dl>

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


<dt>corpus_text</dt>
<dd>vector of text documents, usually contained in the output of prepDocuments</dd>


<dt>topic</dt>
<dd>a single topic number</dd>


<dt>n</dt>
<dd>number of documents to extract</dd>

</dl>

findRepresentativeDocs: A Generic Function to Identify Documents that Load Heavily on a Topic

Description

Usage

Arguments

See Also