Topic Modeling for Communication Research
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.
Pročitajte celu metodu
Prijavite se besplatnim nalogom da biste pročitali ovaj odeljak.
Mapa metoda
Okruženje srodnih metoda — izaberite čvor da biste istraživali.
Izvori
- Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022. link ↗
- Roberts, M. E., Stewart, B. M., Tingley, D., Lucas, C., Leder-Luis, J., Gadarian, S. K., Albertson, B., & Rand, D. G. (2014). Structural topic models for open-ended survey responses. American Journal of Political Science, 58(4), 1064–1082. DOI: 10.1111/ajps.12103 ↗
- 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. DOI: 10.1093/pan/mps028 ↗
Kako citirati ovu stranicu
ScholarGate. (2026, June 22). Topic Modeling for Communication and Media Research. ScholarGate. https://scholargate.app/sr/communication/topic-modeling-communication
Koja metoda?
Postavite ovu metodu pored njoj najbližih srodnika i čitajte ih uporedo — biblioteka polaže knjige na sto; izbor je na vama.
- Automated Content AnalysisCommunication↔ uporedi
- Dictionary-Based Text AnalysisCommunication↔ uporedi
- Semantic Network AnalysisCommunication↔ uporedi
Citirana u
Сличне методе
Uočili ste grešku na ovoj stranici? Prijavite je ili predložite ispravku →