Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Structural Topic Model× | Wordfish Scaling× | |
|---|---|---|
| Ämnesområde | Political Science | Political Science |
| Familj≠ | Process / pipeline | Latent structure |
| Ursprungsår≠ | 2014 | 2008 |
| Upphovsperson≠ | Margaret Roberts, Brandon Stewart & Dustin Tingley | Jonathan Slapin and Sven-Oliver Proksch |
| Typ≠ | Mixed-membership topic model with document-level covariates | Unsupervised latent-position model for word-count data |
| Ursprungskälla≠ | 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 ↗ | 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. DOI ↗ |
| Alias | STM, Structural topic modeling, Covariate-aware topic model, Topic model with metadata | Wordfish text scaling, Poisson scaling of texts, Unsupervised text scaling, Wordfish position estimation |
| Närliggande≠ | 5 | 4 |
| Sammanfattning≠ | 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. | 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. |
| ScholarGateDatamängd ↗ |
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