Порівняння методів

	Topic Modeling for Communication Research	Semantic Network Analysis
Галузь	Communication	Communication
Родина	Machine learning	Process / pipeline
Рік появи	2003	1999
Автор методу	David Blei et al. (LDA); Roberts, Stewart & Tingley (STM)	George Barnett, Marya Doerfel, Steven Corman (communication applications)
Тип	Unsupervised probabilistic model of latent themes in document collections	Network representation of concepts and their co-occurrence in text
Основоположне джерело	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.	Corman, S. R., Kuhn, T., McPhee, R. D., & Dooley, K. J. (2002). Studying complex discursive systems: Centering resonance analysis of communication. Human Communication Research, 28(2), 157–206.
Інші назви	LDA for communication, Structural topic modeling in communication, Topic models for media texts, İletişim Araştırmaları için Konu Modelleme	Text network analysis, Concept co-occurrence network analysis, Centering resonance analysis, Anlamsal Ağ Analizi
Пов'язані	3	4
Підсумок	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.	Semantic network analysis represents the meaning of a text or corpus as a network of concepts connected by their co-occurrence or grammatical proximity, then uses network-analytic measures to reveal which ideas are central, how concepts cluster, and how shared meaning is structured. In communication research it is the standard way to map the conceptual architecture of media coverage, organizational discourse, and public conversation at scale.