方法对比

	Dictionary-Based Text Analysis in Politics	Manifesto Coding
领域	Political Science	Political Science
方法族	Process / pipeline	Process / pipeline
起源年份	2013	2001
提出者	Content-analysis tradition (formalized for political text by Grimmer & Stewart; sentiment dictionaries by Young & Soroka)	Manifesto Research Group / Comparative Manifesto Project (CMP/MARPOR)
类型	Rule-based text scoring from validated word lists	Quantitative content analysis of party manifestos
开创性文献	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.	Budge, I., Klingemann, H.-D., Volkens, A., Bara, J., & Tanenbaum, E. (2001). Mapping Policy Preferences: Estimates for Parties, Electors, and Governments 1945–1998. Oxford: Oxford University Press. ISBN: 9780199244003
别名	Lexicon-based political text analysis, Dictionary methods for political texts, Word-count content analysis of political texts, Political keyword counting	CMP coding, MARPOR coding, Manifesto content analysis, Party manifesto coding
相关	5	4
摘要	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.	Manifesto coding is the quantitative content-analysis methodology of the Comparative Manifesto Project (CMP/MARPOR) for measuring parties' policy preferences from their election manifestos. Trained coders break each manifesto into quasi-sentences and assign every unit to one of a fixed set of policy categories. Counting how often each category appears yields salience measures, and combining pro- and anti- categories produces position scores such as the left–right RILE index, giving comparable estimates of party positions across more than fifty democracies since 1945.