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

Authors

16.5K

  1. DM
    David M. Blei

    Princeton University, United States

    57 publications · 53.1K citations

    41

    h-index

  2. JH
    Jiawei Han

    University of Illinois Urbana-Champaign, United States

    43 publications · 3.3K citations

    28

    h-index

  3. EP
    Eric P. Xing

    Carnegie Mellon University, United States

    36 publications · 3.3K citations

    28

    h-index

  4. CZ
    ChengXiang Zhai

    University of Illinois Urbana-Champaign, United States

    34 publications · 6.1K citations

    26

    h-index

  5. YH
    Yulan He

    Aston University, United Kingdom

    35 publications · 2.8K citations

    26

    h-index

1–5 of 16.5K

Venues

  1. arXiv (Cornell University)

    Repository · 216 publications · 11.6K citations

    46

    h-index

  2. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 87 publications · 7K citations

    39

    h-index

  3. Information Processing & Management

    86 publications · 5.1K citations

    38

    h-index

  4. Expert Systems with Applications

    83 publications · 5.5K citations

    38

    h-index

  5. IEEE Access

    118 publications · 4.7K citations

    37

    h-index

1–5