Xiaofang Jin¹, Yuying Yang^1,*, Yinan Wu¹, Ying Xu²

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5225-5242, 2024, DOI:10.32604/cmc.2024.050384

Abstract The emergence of new media in various fields has continuously strengthened the social aspect of social media. Netizens tend to express emotions in social interactions, and many people even use satire, metaphors, and other techniques to express some negative emotions, it is necessary to detect sarcasm in social comment data. For sarcasm, the more reference data modalities used, the better the experimental effect. This paper conducts research on sarcasm detection technology based on image-text fusion data. To effectively utilize the features of each modality, a feature reconstruction output algorithm is proposed. This algorithm is based… More >

Derwin Suhartono^1,*, 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… More >

Abdul Rahaman Wahab Sait^1,*, Mohamad Khairi Ishak²

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2553-2567, 2023, DOI:10.32604/csse.2023.029603

Abstract Sentiment analysis (SA) is the procedure of recognizing the emotions related to the data that exist in social networking. The existence of sarcasm in textual data is a major challenge in the efficiency of the SA. Earlier works on sarcasm detection on text utilize lexical as well as pragmatic cues namely interjection, punctuations, and sentiment shift that are vital indicators of sarcasm. With the advent of deep-learning, recent works, leveraging neural networks in learning lexical and contextual features, removing the need for handcrafted feature. In this aspect, this study designs a deep learning with natural… More >