stm

R package stm: Estimation of the Structural Topic Model. The Structural Topic Model (STM) allows researchers to estimate topic models with document-level covariates. The package also includes tools for model selection, visualization, and estimation of topic-covariate regressions. Methods developed in Roberts et al (2014) <doi:10.1111/ajps.12103> and Roberts et al (2016) <doi:10.1080/01621459.2016.1141684>. Vignette is Roberts et al (2019) <doi:10.18637/jss.v091.i02>.

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References in zbMATH (referenced in 8 articles , 1 standard article )

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  1. David Ardia, Keven Bluteau, Samuel Borms, Kris Boudt: The R package sentometrics to compute, aggregate and predict with textual sentiment (2021) arXiv
  2. Samuel Borms, David Ardia, Keven Bluteau, Kris Boudt, Jeroen Van Pelt, Andres Algaba: The R Package sentometrics to Compute, Aggregate, and Predict with Textual Sentiment (2021) not zbMATH
  3. Symitsi, Efthymia; Stamolampros, Panagiotis; Daskalakis, George; Korfiatis, Nikolaos: The informational value of employee online reviews (2021)
  4. Chung-hong Chan; Marius Sältzer: oolong: An R package for validating automated content analysis tools (2020) not zbMATH
  5. Jonas Rieger: ldaPrototype: A method in R to get a Prototype of multiple Latent Dirichlet Allocations (2020) not zbMATH
  6. Modesto Escobar, Luis Martinez-Uribe: Network Coincidence Analysis: The netCoin R Package (2020) not zbMATH
  7. Margaret Roberts; Brandon Stewart; Dustin Tingley: stm: An R Package for Structural Topic Models (2019) not zbMATH
  8. Benoit K, Watanabe K, Wang H, Nulty P, Obeng A, Müller S, Matsuo A: quanteda: An R package for the quantitative analysis of textual data (2018) not zbMATH