Results 271 to 280 of about 4,714,839 (322)

Deep Learning Multimodal Sarcasm Detection in Social Media Comments: The Role of Memes and Emojis

Journal of Artificial Intelligence and Technology
Social media has become a crucial platform for interaction, information exchange, and market analysis. Businesses and researchers rely on it for sentiment and emotion analysis, yet sarcasm detection remains a major challenge due to its ability to alter ...
E. D. Wahyuni, Tri Lathif Mardi Suryanto, Heidy Arviani   +2 more
semanticscholar   +1 more source

SemIRNet: A Semantic Irony Recognition Network for Multimodal Sarcasm Detection

2025 10th International Conference on Information and Network Technologies (ICINT)
Aiming at the problem of difficulty in accurately identifying graphical implicit correlations in multimodal irony detection tasks, this paper proposes a Semantic Irony Recognition Network (SemIRNet).
Jingxuan Zhou, Yuehao Wu, Yibo Zhang, Yeyubei Zhang, Yunchong Liu, Bolin Huang, Chunhong Yuan   +6 more
semanticscholar   +1 more source

Learning Multitask Commonness and Uniqueness for Multimodal Sarcasm Detection and Sentiment Analysis in Conversation

IEEE Transactions on Artificial Intelligence
Sarcasm is a form of figurative language device to express human inner feelings, where the author writes the positive sentence on surface form, while he/she actually expresses negative sentiment, vice versa.
Yazhou Zhang, Yang Yu, Dongming Zhao, Zuhe Li, Bo Wang, Yuexian Hou, P. Tiwari, Jin Qin   +7 more
semanticscholar   +1 more source

Leveraging Transitions of Emotions for Sarcasm Detection

Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020
One popular thread of research in computational sarcasm detection involves modeling sarcasm as a contrast between positive and negative sentiment polarities or exploring more fine-grained categories of emotions such as happiness, sadness, surprise, and so on. Most current models, however, treat these affective features independently, without regard for
Ameeta Agrawal, Aijun An, Manos Papagelis   +2 more
openaire   +1 more source

Fusion and Discrimination: A Multimodal Graph Contrastive Learning Framework for Multimodal Sarcasm Detection

IEEE Transactions on Affective Computing
Identifying sarcastic clues from both textual and visual information has become an important research issue, called Multimodal Sarcasm Detection. In this article, we investigate multimodal sarcasm detection from a novel perspective, where a multimodal ...
Bin Liang, Lin Gui, Yulan He, Erik Cambria, Ruifeng Xu   +4 more
semanticscholar   +1 more source

Sarcasm Detection

Sarcasm is a procedure of verbal irony that is planned to convey ridicule, contempt, or mockery. Because sarcasm can change a statement's meaning, the viewpoint analysis procedure is susceptible to mistakes. Sarcastic remarks simply have reduced the effectiveness of sentiment estimation, according to the prior study.
Himani Pokhriyal, Goonjan Jain
openaire   +1 more source

Is Sarcasm Detection A Step-by-Step Reasoning Process in Large Language Models?

AAAI Conference on Artificial Intelligence
Elaborating a series of intermediate reasoning steps significantly improves the ability of large language models (LLMs) to solve complex problems, as such steps would evoke LLMs to think sequentially.
Ben Yao, Yazhou Zhang, Qiuchi Li, Jing Qin   +3 more
semanticscholar   +1 more source

Towards Explainable Sarcasm Detection in Low-Resource Language using Bi-LSTM

European Conference on Cognitive Ergonomics
Sarcasm means using humor to mock someone or something. The task of understanding sarcasm is very challenging because it carries double meaning and can often lead to confusion and misunderstanding.
Samia Muntaha, Md. Sajjatul Islam, Nahida Zakir   +2 more
semanticscholar   +1 more source

Leveraging Generative Large Language Models with Visual Instruction and Demonstration Retrieval for Multimodal Sarcasm Detection

North American Chapter of the Association for Computational Linguistics
Multimodal sarcasm detection aims to identify sarcasm in the given image-text pairs and has wide applications in the multimodal domains. Previous works primarily design complex network structures to fuse the image-text modality features for ...
Binghao Tang, Boda Lin, Haolong Yan, Si Li   +3 more
semanticscholar   +1 more source

IRONIC: Coherence-Aware Reasoning Chains for Multi-Modal Sarcasm Detection

arXiv.org
Interpreting figurative language such as sarcasm across multi-modal inputs presents unique challenges, often requiring task-specific fine-tuning and extensive reasoning steps.
Aashish Anantha Ramakrishnan, Aadarsh Anantha Ramakrishnan, Dongwon Lee   +2 more
semanticscholar   +1 more source