This package implements the Structural Topic and Sentiment-Discourse (STS) model, which 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.
Author: Shawn Mankad and Li Chen
Maintainer: Shawn Mankad smankad@ncsu.edu
Function to fit the model: sts
Functions for Post-Estimation: estimateRegns
topicExclusivity topicSemanticCoherence
heldoutLikelihood plotRepresentativeDocs
findRepresentativeDocs printTopWords
plot.STS
Chen L. and Mankad, S. (2024) "A Structural Topic and Sentiment-Discourse Model for Text Analysis" Management Science.
sts