Text-based Emotion Detection

Text-Based Emotion Detection is a computational methodology within affective computing that identifies and classifies human emotional states from textual data by analyzing linguistic patterns, lexical choices, and contextual semantics.

943

Publications

60.8K

Citations

2.4K

Authors

870

Institutions

Publications per year

2017–2026

599

Authors

2.4K

  1. SM
    Saif M. Mohammad

    National Research Council Canada, Canada

    17 publications · 5K citations

    16

    h-index

  2. EC
    Erik Cambria

    Nanyang Technological University, Singapore

    11 publications · 2.8K citations

    11

    h-index

  3. SL
    Shoushan Li

    Soochow University, China

    11 publications · 872 citations

    11

    h-index

  4. FR
    Fuji Ren

    Tokushima University, Japan

    11 publications · 481 citations

    11

    h-index

  5. HP
    Helmut Prendinger

    National Institute of Informatics, Japan

    10 publications · 640 citations

    10

    h-index

1–5 of 2.4K

Venues

  1. arXiv (Cornell University)

    Repository · 39 publications · 1.5K citations

    20

    h-index

  2. IEEE Access

    20 publications · 1.2K citations

    17

    h-index

  3. Procedia Computer Science

    16 publications · 636 citations

    15

    h-index

  4. IEEE Transactions on Affective Computing

    TAC · 12 publications · 2.9K citations

    13

    h-index

  5. Expert Systems with Applications

    11 publications · 1K citations

    11

    h-index

1–5