Krahasoni metodat

	Supervised Text Classification	Dictionary-Based Text Analysis in Politics
Fusha	Political Science	Political Science
Familja	Process / pipeline	Process / pipeline
Viti i origjinës	2013	2013
Krijuesi	Machine-learning classification tradition (formalized for political text by Grimmer & Stewart; category-proportion variant by Hopkins & King)	Content-analysis tradition (formalized for political text by Grimmer & Stewart; sentiment dictionaries by Young & Soroka)
Lloji	Supervised machine-learning classification of documents	Rule-based text scoring from validated word lists
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.	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.
Emërtime të tjera	Supervised document classification, Text categorization, Automated text coding, Supervised content analysis	Lexicon-based political text analysis, Dictionary methods for political texts, Word-count content analysis of political texts, Political keyword counting
Të lidhura	5	5
Përmbledhja	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.	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.