Palak Bari, Gurnur Bedi, Khushi Joshi, Anupama Jawale^*

Journal on Artificial Intelligence, Vol.7, pp. 499-508, 2025, DOI:10.32604/jai.2025.072531 - 17 November 2025

Abstract This study investigates sarcasm detection in text using a dataset of 8095 sentences compiled from MUStARD and HuggingFace repositories, balanced across sarcastic and non-sarcastic classes. A sequential baseline model (LSTM) is compared with transformer-based models (RoBERTa and XLNet), integrated with attention mechanisms. Transformers were chosen for their proven ability to capture long-range contextual dependencies, whereas LSTM serves as a traditional benchmark for sequential modeling. Experimental results show that RoBERTa achieves 0.87 accuracy, XLNet 0.83, and LSTM 0.52. These findings confirm that transformer architectures significantly outperform recurrent models in sarcasm detection. Future work will incorporate multimodal More >

Kexuan Niu, Xiameng Si^*, Xiaojie Qi, Haiyan Kang

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 2051-2070, 2025, DOI:10.32604/cmc.2025.065377 - 29 August 2025

Abstract Sarcasm detection is a complex and challenging task, particularly in the context of Chinese social media, where it exhibits strong contextual dependencies and cultural specificity. To address the limitations of existing methods in capturing the implicit semantics and contextual associations in sarcastic expressions, this paper proposes an event-aware model for Chinese sarcasm detection, leveraging a multi-head attention (MHA) mechanism and contrastive learning (CL) strategies. The proposed model employs a dual-path Bidirectional Encoder Representations from Transformers (BERT) encoder to process comment text and event context separately and integrates an MHA mechanism to facilitate deep interactions between More >

Jian Luo¹, Yaling Li¹, Xueyu Li¹, Xuliang Hu^2,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 877-896, 2025, DOI:10.32604/cmc.2025.061401 - 26 March 2025

Abstract Sarcasm detection in Natural Language Processing (NLP) has become increasingly important, particularly with the rise of social media and non-textual emotional expressions, such as images. Existing methods often rely on separate image and text modalities, which may not fully utilize the information available from both sources. To address this limitation, we propose a novel multimodal large model, i.e., the PKME-MLM (Prior Knowledge and Multi-label Emotion analysis based Multimodal Large Model for sarcasm detection). The PKME-MLM aims to enhance sarcasm detection by integrating prior knowledge to extract useful textual information from images, which is then combined… More >

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 - 20 June 2024

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 - 26 December 2023

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 - 01 August 2022

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 >