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

Authors

273

  1. CV
    Charles V. Trappey

    National Yang Ming Chiao Tung University, Taiwan

    4 publications · 163 citations

    4

    h-index

  2. AJ
    Amy J.C. Trappey

    National Tsing Hua University, Taiwan

    4 publications · 163 citations

    4

    h-index

  3. BY
    Byungun Yoon

    Dongguk University, South Korea

    3 publications · 155 citations

    3

    h-index

  4. SP
    Sangsung Park

    Korea University, South Korea

    3 publications · 105 citations

    3

    h-index

  5. TD
    Tuğrul Daim

    Portland State University, United States

    2 publications · 173 citations

    3

    h-index

1–5 of 273

Venues

  1. Technological Forecasting and Social Change

    12 publications · 799 citations

    12

    h-index

  2. Scientometrics

    10 publications · 409 citations

    11

    h-index

  3. Sustainability

    9 publications · 337 citations

    9

    h-index

  4. IEEE Transactions on Engineering Management

    6 publications · 178 citations

    7

    h-index

  5. World Patent Information

    3 publications · 273 citations

    4

    h-index

1–5