International Journal of Analysis and Applications ISSN 2291-8639 (Online)

Research Article

Optimizing Deep Learning-Based Hate Speech Classification on Social Media Through Frame Semantic Theory and Hyperparameter Tuning

  • Korakoch Mounggam
  • Kittipol Wisaeng*

    * Corresponding author

    Abstract

    This study uses deep learning, frame semantic theory, and precise hyperparameter tuning to classify social media hate speech. Three carefully selected hate speech datasets, arranged according to a frame semantic framework, are used to categorize opposing content, helping to better grasp context than simply looking for words. The Synthetic Minority Over-sampling Technique (SMOTE) enhanced minority-class detection to mitigate class imbalance. To find optimal configurations, deep learning architectures, activation functions, and data split algorithms were tested. The best approach used 70% of the data for training and 30% for testing, a model with hidden layers, a Rectifier activation function, and 10 epochs, achieving 96.30% accuracy, 96.95% recall, 99.15% precision, and 0.98 F1-score. These findings show that frame semantic theory, deep learning, and rigorous hyperparameter optimization can significantly improve social media hate speech detection systems. The method lays the foundation for context-aware and socially responsible content control.

    How to Cite

    K. Mounggam, K. Wisaeng, Optimizing Deep Learning-Based Hate Speech Classification on Social Media Through Frame Semantic Theory and Hyperparameter Tuning, Int. J. Anal. Appl. 24 (2026), 86. https://doi.org/10.28924/2291-8639-24-2026-86

    BibTeX
    @article{Mounggam2026,
  author  = {Mounggam, Korakoch and Wisaeng, Kittipol},
  title   = {Optimizing Deep Learning-Based Hate Speech Classification on Social Media Through Frame Semantic Theory and Hyperparameter Tuning},
  journal = {Int. J. Anal. Appl.},
  volume  = {24},
  year    = {2026},
  pages   = {86},
  doi     = {10.28924/2291-8639-24-2026-86}
}

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