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	Topic Modeling for Communication Research	Dictionary-Based Text Analysis
Ämnesområde	Communication	Communication
Familj	Machine learning	Process / pipeline
Ursprungsår	2003	2003
Upphovsperson	David Blei et al. (LDA); Roberts, Stewart & Tingley (STM)	Lexicon tradition (Pennebaker LIWC; General Inquirer)
Typ	Unsupervised probabilistic model of latent themes in document collections	Word-count text measurement against predefined category dictionaries
Ursprungskälla	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.
Alias	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
Närliggande	3	4
Sammanfattning	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.