Sammenlign metoder

	Automated Content Analysis	Topic Modeling for Communication Research
Fagområde	Communication	Communication
Familie	Process / pipeline	Machine learning
Oprindelsesår	2013	2003
Ophavsperson	Justin Grimmer & Brandon Stewart (synthesis)	David Blei et al. (LDA); Roberts, Stewart & Tingley (STM)
Type	Computational pipeline for measuring features of large text corpora	Unsupervised probabilistic model of latent themes in document collections
Oprindelig kilde	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.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.
Aliasser	Computational content analysis, Text-as-data analysis, Automated text analysis, Otomatik İçerik Analizi	LDA for communication, Structural topic modeling in communication, Topic models for media texts, İletişim Araştırmaları için Konu Modelleme
Relaterede	4	3
Resumé	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.	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.