Structural Topic Models
Structural Topic Model is a probabilistic topic modeling framework that extends the Latent Dirichlet Allocation paradigm by incorporating document-level metadata as covariates to influence topic prevalence and lexical variation, thereby enabling more nuanced inference of thematic structures from text collections.
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23.6K
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1.1K
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2017–2026
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Structural Topic Models for Open‐Ended Survey Responses
Margaret E. Roberts, Brandon Stewart, Dustin Tingley et al. · American Journal of Political Science · 2014 · 1.9K citations
Computer-Assisted Text Analysis for Comparative Politics
Christopher Lucas, Richard A. Nielsen, Margaret E. Roberts et al. · Political Analysis · 2015 · 485 citations · Full text
Introduction—Topic models: What they are and why they matter
John W. Mohr, Petko Bogdanov · Poetics · 2013 · 484 citations
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Education University of Hong Kong
Tai Po, Hong Kong
10 h-index
24 publications
2.3K citations
University of Wisconsin–Madison
Madison, United States
8 h-index
37 publications
1.6K citations
Tsinghua University
Beijing, China
8 h-index
16 publications
510 citations

Harvard University Press
Cambridge, United States
7 h-index
16 publications
8.5K citations
Stanford University
Stanford, United States
6 h-index
13 publications
1.3K citations
University of Bergen
Bergen, Norway
6 h-index
14 publications
933 citations
University College London
London, United Kingdom
6 h-index
22 publications
975 citations
Chinese University of Hong Kong
Hong Kong, Hong Kong
6 h-index
12 publications
323 citations
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