So sánh phương pháp

	Dictionary-Based Text Analysis in Politics	Structural Topic Model
Lĩnh vực	Political Science	Political Science
Họ	Process / pipeline	Process / pipeline
Năm ra đời	2013	2014
Người khởi xướng	Content-analysis tradition (formalized for political text by Grimmer & Stewart; sentiment dictionaries by Young & Soroka)	Margaret Roberts, Brandon Stewart & Dustin Tingley
Loại	Rule-based text scoring from validated word lists	Mixed-membership topic model with document-level covariates
Công trình gốc	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.	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.
Tên gọi khác	Lexicon-based political text analysis, Dictionary methods for political texts, Word-count content analysis of political texts, Political keyword counting	STM, Structural topic modeling, Covariate-aware topic model, Topic model with metadata
Liên quan	5	5
Tóm tắt	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.	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.