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cff-version: 1.2.0
message: 'To cite package "sts" in publications use:'
type: software
license: MIT
title: 'sts: Estimation of the Structural Topic and Sentiment-Discourse Model for
  Text Analysis'
version: '1.6'
doi: 10.32614/CRAN.package.sts
abstract: 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 (2025) <https://doi.org/10.1287/mnsc.2022.00261>.
authors:
- family-names: Mankad
  given-names: Shawn
  email: smankad@ncsu.edu
- family-names: Chen
  given-names: Li
  email: li.chen@cornell.edu
repository: https://mankad-research.r-universe.dev
repository-code: https://github.com/mankad-research/sts_r
commit: da7d4bd52b767ed548f198e83386a6606705f87b
url: https://github.com/mankad-research/sts_r
date-released: '2026-10-23'
contact:
- family-names: Mankad
  given-names: Shawn
  email: smankad@ncsu.edu