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.
방법 전문 읽기
무료 계정으로 로그인하면 이 섹션을 읽을 수 있습니다.
방법 지도
관련 방법들로 이루어진 인접 영역 — 노드를 선택해 살펴보세요.
출처
- 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 ↗
이 페이지 인용 방법
ScholarGate. (2026, June 22). Topic Modeling for Communication and Media Research. ScholarGate. https://scholargate.app/ko/communication/topic-modeling-communication
어떤 방법일까요?
이 방법을 가장 가까운 동류의 방법들과 나란히 놓고 비교해 보세요 — 라이브러리는 책을 펼쳐 놓을 뿐, 선택은 여러분의 몫입니다.
- Automated Content AnalysisCommunication↔ 비교
- Dictionary-Based Text AnalysisCommunication↔ 비교
- Semantic Network AnalysisCommunication↔ 비교