Package: sts 1.4
Shawn Mankad
sts: Estimation of the Structural Topic and Sentiment-Discourse Model for Text Analysis
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>.
Authors:
sts_1.4.tar.gz
sts_1.4.tar.gz(r-4.5-noble)sts_1.4.tar.gz(r-4.4-noble)
sts_1.4.tgz(r-4.4-emscripten)sts_1.4.tgz(r-4.3-emscripten)
sts.pdf |sts.html✨
sts/json (API)
| # Install 'sts' in R: |
| install.packages('sts', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 7 days agofrom:67c0b3f6f5. Checks:2 OK. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Jan 27 2025 |
| R-4.5-linux-x86_64 | OK | Jan 27 2025 |
Exports:esthcppestimateRegnsfindRepresentativeDocsheldoutLikelihoodlgaecpplpbdcppplotRepresentativeDocsprintRegnTablesprintTopWordsststopicExclusivitytopicSemanticCoherence
Dependencies:BHclicodetoolscolorspacedata.tabledoParallelfansifarverfastmatchforeachggplot2glmnetgluegtableisobandISOcodesiteratorsjsonlitelabelinglatticeldalifecyclemagrittrMASSMatrixmatrixStatsmgcvmunsellmvtnormnlmeNLPpillarpkgconfigquadprogquantedaR6RColorBrewerRcppRcppArmadilloRcppEigenrlangscalesshapeslamSnowballCstmstopwordsstringistringrsurvivaltibbletmutf8vctrsviridisLitewithrxml2yaml
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| A Structural Topic and Sentiment-Discourse Model for Text Analysis | sts-package |
| Regression Table Estimation | estimateRegns |
| Function for Identifying Documents that Load Heavily on a Topic | findRepresentativeDocs |
| Compute Heldout Log-Likelihood | heldoutLikelihood |
| Function for plotting STS objects | plot.STS |
| Plot Documents that Load Heavily on a Topic | plotRepresentativeDocs |
| Print Estimated Regression Tables | printRegnTables |
| Print Top Words that Load Heavily on each Topic | printTopWords |
| Variational EM for the Structural Topic and Sentiment-Discourse (STS) Model | sts |
| Summary Function for the STS objects | print.STS summary.STS |
| Compute Exclusivity | topicExclusivity |
| Compute Semantic Coherence | topicSemanticCoherence |