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Structural Topic Model

The Structural Topic Model (STM) is a text-as-data method that discovers latent themes in a corpus while letting document metadata — party, time, gender, treatment condition — shape those themes. Introduced by Roberts, Stewart, Tingley and colleagues in 2014, it generalizes correlated topic modeling so that topic prevalence (how much a document is about a topic) and topic content (the words used to express a topic) can both depend on covariates. The result is a single model that simultaneously estimates topics and how their use varies across known groups, with uncertainty.

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Fuentes

  1. Roberts, M. E., Stewart, B. M., Tingley, D., Lucas, C., Leder-Luis, J., Gadarian, S. K., Albertson, B., & Rand, D. G. (2014). Structural Topic Models for Open-Ended Survey Responses. American Journal of Political Science, 58(4), 1064–1082. DOI: 10.1111/ajps.12103
  2. Roberts, M. E., Stewart, B. M., & Tingley, D. (2019). stm: An R Package for Structural Topic Models. Journal of Statistical Software, 91(2), 1–40. DOI: 10.18637/jss.v091.i02
  3. Grimmer, J., & Stewart, B. M. (2013). Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts. Political Analysis, 21(3), 267–297. DOI: 10.1093/pan/mps028

Cómo citar esta página

ScholarGate. (2026, June 22). Structural Topic Model (Topic Modeling with Document-Level Covariates). ScholarGate. https://scholargate.app/es/political-science/structural-topic-model

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ScholarGateStructural Topic Model (Structural Topic Model (Topic Modeling with Document-Level Covariates)). Recuperado el 2026-06-24 de https://scholargate.app/es/political-science/structural-topic-model · Conjunto de datos: https://doi.org/10.5281/zenodo.20539026