Krahasoni metodat

	Dictionary-Based Text Analysis in Politics	Wordfish Scaling
Fusha	Political Science	Political Science
Familja	Process / pipeline	Latent structure
Viti i origjinës	2013	2008
Krijuesi	Content-analysis tradition (formalized for political text by Grimmer & Stewart; sentiment dictionaries by Young & Soroka)	Jonathan Slapin and Sven-Oliver Proksch
Lloji	Rule-based text scoring from validated word lists	Unsupervised latent-position model for word-count data
Burimi themelues	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.	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.
Emërtime të tjera	Lexicon-based political text analysis, Dictionary methods for political texts, Word-count content analysis of political texts, Political keyword counting	Wordfish text scaling, Poisson scaling of texts, Unsupervised text scaling, Wordfish position estimation
Të lidhura	5	4
Përmbledhja	Dictionary-based text analysis scores documents by counting how often they use words from a predefined, validated list — a dictionary or lexicon — tied to a concept such as sentiment, emotion, or a policy area. Each document's score is essentially the rate at which dictionary terms appear, so a corpus of speeches, news articles, or manifestos can be measured for tone or thematic emphasis quickly and transparently. It is the simplest and most interpretable family of automated content-analysis methods, and Grimmer and Stewart treat it as a baseline against which more elaborate text-as-data tools are judged.	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.