Structural Topic Models

Structural Topic Model is a probabilistic topic modeling framework that extends the Latent Dirichlet Allocation paradigm by incorporating document-level metadata as covariates to influence topic prevalence and lexical variation, thereby enabling more nuanced inference of thematic structures from text collections.

389

Publications

23.6K

Citations

1.1K

Authors

562

Institutions

Publications per year

2017–2026

278

Authors

1.1K

  1. XC
    Xieling Chen

    Education University of Hong Kong, Hong Kong SAR China

    10 publications · 899 citations

    10

    h-index

  2. HX
    Haoran Xie

    Education University of Hong Kong, Hong Kong SAR China

    9 publications · 842 citations

    9

    h-index

  3. ME
    Margaret E. Roberts

    University of California San Diego, United States

    8 publications · 4.6K citations

    8

    h-index

  4. BS
    Brandon Stewart

    Princeton University, United States

    8 publications · 4.6K citations

    8

    h-index

  5. DT
    Dustin Tingley

    Harvard University Press, United States

    8 publications · 4.6K citations

    8

    h-index

1–5 of 1.1K

Venues

  1. Information Processing & Management

    10 publications · 761 citations

    10

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  2. Expert Systems with Applications

    6 publications · 406 citations

    6

    h-index

  3. Scientometrics

    6 publications · 372 citations

    6

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  4. Information Sciences

    5 publications · 179 citations

    5

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  5. Sustainability

    5 publications · 183 citations

    5

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1–5