Wordfish Scaling
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.
Allikakirje
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- 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 10.1111/j.1540-5907.2008.00338.x
- Lowe, W., & Benoit, K. (2013). Validating Estimates of Latent Traits from Textual Data Using Human Judgment as a Benchmark. Political Analysis, 21(3), 298–313. · DOI 10.1093/pan/mpt002
- 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 10.1093/pan/mps028
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