Сравнение методов

	Wordfish Scaling	Wordfish
Область	Political Science	Психометрия
Семейство	Latent structure	Latent structure
Год появления	2008	2008
Автор метода	Jonathan Slapin and Sven-Oliver Proksch	Jonathan Slapin, Svenja-Sophia Proksch
Тип	Unsupervised latent-position model for word-count data	Generative text model for dimension reduction
Основополагающий источник	Slapin, J. B., & Proksch, S.-O. (2008). A Scaling Model for Estimating Time-Series Party Positions from Texts. American Journal of Political Science, 52(3), 705–722.	Slapin, J. B., & Proksch, S. O. (2008). A scaling model for estimating time-series party positions from texts. Journal of Politics, 70(3), 554-569.
Другие названия	Wordfish text scaling, Poisson scaling of texts, Unsupervised text scaling, Wordfish position estimation	—
Связанные	4	5
Сводка	Wordfish scaling is an unsupervised text-as-data method that estimates a single latent position for each political document — a party manifesto, a legislative speech, a press release — directly from its word frequencies, without any reference texts or hand coding. Introduced by Slapin and Proksch in 2008, it models word counts as draws from a Poisson distribution whose rate depends on a document position and word-specific parameters, recovering, for example, a left–right ordering of parties purely from how often each word appears in each text.	Wordfish is a statistical model for scaling documents on latent dimensions, developed by Slapin and Proksch (2008). Unlike reference-based methods like Wordscores, Wordfish uses a Poisson generative model to jointly estimate word frequencies and document positions without requiring reference texts or manual annotation. It is particularly useful for estimating time-series changes in policy positions and can scale documents from multiple languages simultaneously.