Сравнение методов

	Topic Modeling for Communication Research	Dictionary-Based Text Analysis
Область	Communication	Communication
Семейство	Machine learning	Process / pipeline
Год появления	2003	2003
Автор метода	David Blei et al. (LDA); Roberts, Stewart & Tingley (STM)	Lexicon tradition (Pennebaker LIWC; General Inquirer)
Тип	Unsupervised probabilistic model of latent themes in document collections	Word-count text measurement against predefined category dictionaries
Основополагающий источник	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.	Pennebaker, J. W., Mehl, M. R., & Niederhoffer, K. G. (2003). Psychological aspects of natural language use: Our words, our selves. Annual Review of Psychology, 54, 547–577.
Другие названия	LDA for communication, Structural topic modeling in communication, Topic models for media texts, İletişim Araştırmaları için Konu Modelleme	Lexicon-based text analysis, Word-count text analysis, Dictionary method for content analysis, Sözlük Tabanlı Metin Analizi
Связанные	3	4
Сводка	Topic modeling is an unsupervised technique for discovering the latent themes that run through a large collection of documents, representing each document as a mixture of topics and each topic as a distribution over words. In communication research it surfaces the issues, frames, and themes in news archives, social media, and political text at a scale no manual reading can match, with Latent Dirichlet Allocation (LDA) and the Structural Topic Model (STM) as the dominant variants.	Dictionary-based text analysis measures concepts in text by counting how often words belonging to predefined category lists — dictionaries — appear in each document. It is the workhorse lexicon method behind tools like LIWC and the General Inquirer, prized for its transparency and scalability: a category score is simply the share of a document's words that match the category's word list.