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) <doi:10.1287/mnsc.2022.00261>.
| Version: |
1.6 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
Rcpp, glmnet, matrixStats, slam, foreach, doParallel, parallel, Matrix, mvtnorm, ggplot2, tm, SnowballC, graphics, stats |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Suggests: |
R.rsp |
| Published: |
2026-10-23 |
| DOI: |
10.32614/CRAN.package.sts |
| Author: |
Shawn Mankad [aut, cre],
Li Chen [aut],
Margaret E. Roberts [ctb, cph] (Author of adapted code from the stm
package),
Brandon M. Stewart [ctb, cph] (Author of adapted code from the stm
package),
Dustin Tingley [ctb, cph] (Author of adapted code from the stm package) |
| Maintainer: |
Shawn Mankad <smankad at ncsu.edu> |
| BugReports: |
https://github.com/mankad-research/sts_r/issues |
| License: |
MIT + file LICENSE |
| Copyright: |
see file COPYRIGHTS |
| URL: |
https://github.com/mankad-research/sts_r |
| NeedsCompilation: |
yes |
| Materials: |
NEWS |
| CRAN checks: |
sts results |