Patent Topic Modeling
Patent Topic Modeling is a computational text analysis technique that identifies latent thematic structures within patent documents by applying probabilistic or machine learning models to extract and categorize recurring patterns of terminology and innovation topics across large-scale patent datasets.
97
Publications
4.8K
Citations
273
Authors
130
Institutions
Publications per year
2017–2026
70
Parents
97
1–5 of 97
273
4
h-index
4
h-index
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130
Tsinghua University
Beijing, China
8 h-index
21 publications
987 citations
Korea University
Seoul, South Korea
7 h-index
18 publications
706 citations

Beijing Institute of Technology
Beijing, China
5 h-index
17 publications
524 citations

Beijing University of Technology
Beijing, China
5 h-index
9 publications
360 citations

National Yang Ming Chiao Tung University
Hsinchu, Taiwan
4 h-index
4 publications
163 citations

Portland State University
Portland, United States
4 h-index
4 publications
499 citations
National Tsing Hua University
Hsinchu, Taiwan
4 h-index
7 publications
311 citations

Queensland University of Technology
Brisbane, Australia
3 h-index
3 publications
84 citations
1–8 of 130
12 publications · 799 citations
12
h-index
10 publications · 409 citations
11
h-index
9 publications · 337 citations
9
h-index
6 publications · 178 citations
7
h-index
3 publications · 273 citations
4
h-index
1–5