Supervised Topic Modeling
Supervised Topic Modeling is a topic modeling approach that incorporates labeled data to guide the discovery of latent thematic structures, distinguishing itself from unsupervised methods by leveraging supervision to align topics with predefined outcomes or categories.
119
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8.8K
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392
Authors
191
Institutions
Publications per year
2017–2026
66
Parents
119
David M. Blei, Jon McAuliffe · arXiv (Cornell University) · 2010 · 1.3K citations · Full text
A Latent Variable Model for Geographic Lexical Variation
Jacob Eisenstein, Brendan O’Connor, Noah A. Smith et al. · Figshare · 2018 · 607 citations · Full text
Computing, Geography, Linguistics +17
Discovering Health Topics in Social Media Using Topic Models
Michael J. Paul, Mark Dredze · PLoS ONE · 2014 · 287 citations · Full text
Multi-aspect Sentiment Analysis with Topic Models
Bin Lu, Myle Ott, Claire Cardie et al. · 2011 · 256 citations
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191
Carnegie Mellon University
Pittsburgh, United States
10 h-index
22 publications
4.2K citations
Tsinghua University
Beijing, China
8 h-index
17 publications
1.1K citations
University of Illinois Urbana-Champaign
Urbana, United States
5 h-index
12 publications
293 citations
Massachusetts Institute of Technology
Cambridge, United States
4 h-index
6 publications
342 citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
3 h-index
6 publications
289 citations
University of Cambridge
Cambridge, United Kingdom
3 h-index
5 publications
392 citations
University of Maryland, College Park
College Park, United States
3 h-index
5 publications
229 citations
University of Chicago
Chicago, United States
3 h-index
6 publications
239 citations
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Repository · 4 publications · 953 citations
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3 publications · 52 citations
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Repository · 3 publications · 149 citations
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3 publications · 131 citations
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Repository · 2 publications · 1.4K citations
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