Topic Modeling
Topic Modeling is a statistical text analysis technique within natural language processing that identifies latent thematic structures in a corpus by deriving probabilistic distributions of words and documents, distinguishing itself from other text analytics methods through its unsupervised discovery of recurring semantic patterns without predefined categories.
7.2K
Publications
499.7K
Citations
16.5K
Authors
3.1K
Institutions
Publications per year
2017–2026
3K
7.2K
Bo Pang, Lillian Lee, Shivakumar Vaithyanathan · 2002 · 7K citations · Full text
David M. Blei · Communications of the ACM · 2012 · 5.5K citations
Software Framework for Topic Modelling with Large Corpora
Petr Sojka · 2010 · 3.8K citations · Full text
1–5 of 7.2K
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University of Illinois Urbana-Champaign, United States
34 publications · 6.1K citations
26
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26
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1–5 of 16.5K
3.1K
Carnegie Mellon University
Pittsburgh, United States
58 h-index
356 publications
33.1K citations
Tsinghua University
Beijing, China
55 h-index
424 publications
31.3K citations
University of Illinois Urbana-Champaign
Urbana, United States
55 h-index
352 publications
35K citations
Princeton University
Princeton, United States
45 h-index
121 publications
48.1K citations

Chinese Academy of Sciences
Beijing, China
45 h-index
389 publications
23.7K citations

University of Massachusetts Amherst
Amherst Center, United States
43 h-index
193 publications
29.1K citations
Microsoft Research Asia (China)
Beijing, China
42 h-index
149 publications
17.7K citations
Microsoft (United States)
Redmond, United States
42 h-index
142 publications
11.3K citations
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Repository · 216 publications · 11.6K citations
46
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TKDE · 87 publications · 7K citations
39
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86 publications · 5.1K citations
38
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83 publications · 5.5K citations
38
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118 publications · 4.7K citations
37
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