Salīdzināt metodes

	Network Text Analysis	Automated Content Analysis
Nozare	Communication	Communication
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	2002	2013
Autors	Corman et al. (centering resonance analysis); network text tradition	Justin Grimmer & Brandon Stewart (synthesis)
Tips	Representation and analysis of text as networks of linked concepts	Computational pipeline for measuring features of large text corpora
Pirmavots	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.	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.
Citi nosaukumi	Text network analysis, Centering resonance analysis, Concept network analysis, Ağ Tabanlı Metin Analizi	Computational content analysis, Text-as-data analysis, Automated text analysis, Otomatik İçerik Analizi
Saistītās	4	4
Kopsavilkums	Network text analysis represents the content of text not as counts of words or topics but as a network of concepts linked by their relationships, then applies social-network methods to reveal which ideas are central and how they connect. Centering resonance analysis (CRA), introduced by Corman and colleagues in 2002, is a leading variant that builds concept networks from the noun phrases that structure discourse.	Automated content analysis is the computational measurement of text features at a scale impossible by hand, using natural-language processing and machine learning to classify, scale, or discover the content of large corpora. Synthesized for the social sciences by Grimmer and Stewart's 2013 'Text as Data,' it spans supervised classification, unsupervised discovery, and scaling, all unified by the principle that automated methods augment but do not replace careful human judgment and validation.