Derwin Suhartono1,*, Alif Tri Handoyo1, Franz Adeta Junior2
CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3637-3657, 2023, DOI:10.32604/cmc.2023.045301
Abstract Sarcasm detection in text data is an increasingly vital area of research due to the prevalence of sarcastic content in online communication. This study addresses challenges associated with small datasets and class imbalances in sarcasm detection by employing comprehensive data pre-processing and Generative Adversial Network (GAN) based augmentation on diverse datasets, including iSarcasm, SemEval-18, and Ghosh. This research offers a novel pipeline for augmenting sarcasm data with Reverse Generative Adversarial Network (RGAN). The proposed RGAN method works by inverting labels between original and synthetic data during the training process. This inversion of labels provides feedback to the generator for generating… More >
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