Dynamic Topic Models

Dynamic Topic Models is a probabilistic topic modeling framework that extends static topic models by incorporating temporal dynamics to capture the evolution of topics over time in a document corpus.

271

Publications

20.6K

Citations

872

Authors

348

Institutions

Publications per year

2017–2026

133

Authors

872

  1. SL
    Shangsong Liang

    Sun Yat-sen University, China

    8 publications · 392 citations

    8

    h-index

  2. ZR
    Zhaochun Ren

    Shandong University, China

    6 publications · 277 citations

    6

    h-index

  3. EY
    Emine Yılmaz

    University College London, United Kingdom

    6 publications · 273 citations

    6

    h-index

  4. DM
    David M. Blei

    Princeton University, United States

    6 publications · 2.9K citations

    6

    h-index

  5. MD
    Maarten de Rijke

    University of Amsterdam, Netherlands

    5 publications · 255 citations

    5

    h-index

1–5 of 872

Venues

  1. arXiv (Cornell University)

    Repository · 11 publications · 667 citations

    10

    h-index

  2. Expert Systems with Applications

    7 publications · 257 citations

    7

    h-index

  3. Neurocomputing

    7 publications · 215 citations

    7

    h-index

  4. Scientometrics

    7 publications · 270 citations

    7

    h-index

  5. Information Processing & Management

    6 publications · 216 citations

    6

    h-index

1–5