Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Structural Topic Model× | Supervised Text Classification× | |
|---|---|---|
| Nozare | Political Science | Political Science |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 2014 | 2013 |
| Autors≠ | Margaret Roberts, Brandon Stewart & Dustin Tingley | Machine-learning classification tradition (formalized for political text by Grimmer & Stewart; category-proportion variant by Hopkins & King) |
| Tips≠ | Mixed-membership topic model with document-level covariates | Supervised machine-learning classification of documents |
| Pirmavots≠ | 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 ↗ | 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 ↗ |
| Citi nosaukumi | STM, Structural topic modeling, Covariate-aware topic model, Topic model with metadata | Supervised document classification, Text categorization, Automated text coding, Supervised content analysis |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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. | Supervised text classification trains a statistical model on documents that humans have hand-labeled, then uses it to assign categories — topic, tone, position, relevance — to the much larger set of unlabeled documents. Unlike dictionary methods, which apply a fixed word list, a supervised classifier learns from examples which textual features predict each category, so it can capture context-dependent and non-obvious cues. Grimmer and Stewart present it as a core text-as-data workflow, and a key insight is that for many political-science questions the goal is not perfect document-by-document labels but accurate estimates of category proportions across a corpus. |
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