Comparer des méthodes

	Structural Topic Model	Modèle de Topics LDA
Domaine	Political Science	Apprentissage profond
Famille	Process / pipeline	Machine learning
Année d'origine	2014	2003
Auteur d'origine	Margaret Roberts, Brandon Stewart & Dustin Tingley	Blei, D. M., Ng, A. Y., & Jordan, M. I.
Type	Mixed-membership topic model with document-level covariates	Probabilistic generative topic model
Source fondatrice	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.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
Alias	STM, Structural topic modeling, Covariate-aware topic model, Topic model with metadata	LDA, Latent Dirichlet Allocation, LDA Topic Modeling, Dirichlet Topic Model
Apparentées	5	5
Résumé	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.	Latent Dirichlet Allocation (LDA) is a probabilistic generative model introduced by Blei, Ng, and Jordan in 2003 that discovers hidden thematic structure in large text collections by representing each document as a mixture of latent topics and each topic as a probability distribution over vocabulary words.