Порівняння методів

	Structural Topic Model	Тематичне моделювання
Галузь	Political Science	Глибоке навчання
Родина	Process / pipeline	Machine learning
Рік появи	2014	1999–2003
Автор методу	Margaret Roberts, Brandon Stewart & Dustin Tingley	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
Тип	Mixed-membership topic model with document-level covariates	Unsupervised generative probabilistic model
Основоположне джерело	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.
Інші назви	STM, Structural topic modeling, Covariate-aware topic model, Topic model with metadata	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
Пов'язані	5	5
Підсумок	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.	Topic Modeling is a family of unsupervised probabilistic techniques for discovering latent thematic structure in large text collections. By learning which words tend to co-occur, models such as Latent Dirichlet Allocation (LDA) automatically surface coherent topics — each represented as a distribution over vocabulary — without requiring labelled data.