Feature Engineering For Text Data

Feature Engineering for Text Data is a domain-specific feature design methodology that systematically transforms raw textual inputs into structured numerical representations through techniques such as tokenization, vectorization, and syntactic or semantic feature extraction to enhance machine learning model performance.

373

Publications

18.4K

Citations

1.2K

Authors

469

Institutions

Publications per year

2017–2026

184

Authors

1.2K

  1. MA
    Marcos André Gonçalves

    Universidade Federal de Minas Gerais, Brazil

    4 publications · 167 citations

    4

    h-index

  2. CP
    Carolyn Penstein Rosé

    Carnegie Mellon University, United States

    4 publications · 287 citations

    4

    h-index

  3. SC
    Sérgio Canuto

    Universidade Federal de Minas Gerais, Brazil

    3 publications · 103 citations

    3

    h-index

  4. DP
    Daniele Pighin

    University of Trento, Italy

    3 publications · 59 citations

    3

    h-index

  5. TC
    Thierson Couto Rosa

    Universidade Federal de Goiás, Brazil

    3 publications · 103 citations

    3

    h-index

1–5 of 1.2K

Venues

  1. Expert Systems with Applications

    9 publications · 819 citations

    9

    h-index

  2. arXiv (Cornell University)

    Repository · 9 publications · 626 citations

    9

    h-index

  3. IEEE Access

    9 publications · 320 citations

    8

    h-index

  4. Information Processing & Management

    6 publications · 570 citations

    6

    h-index

  5. Procedia Computer Science

    5 publications · 204 citations

    5

    h-index

1–5