Results 261 to 270 of about 18,525 (328)

Enhancing Sarcasm Detection Using GAN-BERT with Multi-Task Learning

2025 International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN)
Sarcasm detection remains a complex task in natural language processing due to its nuanced and contextdependent nature. This paper introduces a novel sarcasm detection framework combining GAN-BERT and Multitask Learning (MTL), where sentiment and emotion
Afif Hossain Irfan, Shrabani Das, Jannatul Ferdaus, Md. Tabil Ahammed   +3 more
semanticscholar   +1 more source

Regional Variation in the Use of Sarcasm

Journal of Language and Social Psychology, 2008
College students in New York and Tennessee participated in tasks designed to measure their use of sarcasm. They also provided definitions for the terms irony and sarcasm and completed part of a Sarcasm Self-Report Scale.
Caucci, Gina M., Dress, Megan L., Kreuz, Roger J., Link, Kristen E.   +3 more
exaly   +2 more sources

Optimism, Expectation, or Sarcasm? Multi-Class Hope Speech Detection in Spanish and English

arXiv.org
Hope is a complex and underexplored emotional state that plays a significant role in education, mental health, and social interaction. Unlike basic emotions, hope manifests in nuanced forms ranging from grounded optimism to exaggerated wishfulness or ...
S. Butt, F. Balouchzahi, Ahmad Imam Amjad, Maaz Amjad, Hector G. Ceballos, Salud María Jiménez-Zafra   +5 more
semanticscholar   +1 more source

Elevating Knowledge-Enhanced Entity and Relationship Understanding for Sarcasm Detection

IEEE Transactions on Knowledge and Data Engineering
Sarcasm thrives on popular social media platforms such as Twitter and Reddit, where users frequently employ it to convey emotions in an ironic or satirical manner.
Xiao-Bao Wang, Yu-Jing Wang, Dong-Xiao He, Zhe Yu, Ya-Wen Li, Longbiao Wang, J. Dang, Di Jin   +7 more
semanticscholar   +1 more source

Enhancing Semantic Awareness by Sentimental Constraint With Automatic Outlier Masking for Multimodal Sarcasm Detection

IEEE transactions on multimedia
Multimodal sarcasm detection, aiming to uncover sarcastic sentiment behind multimodal data, has gained substantial attention in multimodal communities. Recent advancements in multimodal sarcasm detection (MSD) methods have primarily focused on modality ...
Shao-Zu Yuan, Yiwei Wei, Hengyang Zhou, Qin-Fu Xu, Meng Chen, Xiaodong He   +5 more
semanticscholar   +1 more source

Multi-Modal Sarcasm Detection via Knowledge-Aware Focused Graph Convolutional Networks

ACM Trans. Multim. Comput. Commun. Appl.
Multi-Modal Sarcasm Detection (MSD) aims to combine multiple modal information to identify implicit sarcastic sentiment. However, the significance of commonsense knowledge in implicit emotion recognition has been frequently overlooked.
Xing-Jie Zhuang, Fengling Zhou, Zhixin Li   +2 more
semanticscholar   +1 more source

Multi-modal Sarcasm Detection on Social Media via Multi-Granularity Information Fusion

ACM Trans. Multim. Comput. Commun. Appl.
The rising popularity of diverse social media platforms, commonly utilized by individuals to articulate their emotions in everyday interactions, has spurred a growing interest in the task of multi-modal sarcasm detection (MSD).
Lisong Ou, Zhixin Li
semanticscholar   +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, Rui-Feng Xu   +4 more
semanticscholar   +1 more source

Detecting Sarcasm in Text

2019
Sarcasm is a nuanced form of speech extensively employed in various online platforms such as social networks, micro-blogs etc. and sarcasm detection refers to predicting whether the text is sarcastic or not. Detecting sarcasm in text is among the major issues facing sentiment analysis.
Sakshi Thakur, Sarbjeet Singh, Makhan Singh   +2 more
openaire   +2 more sources

SarcasmBench: Towards Evaluating Large Language Models on Sarcasm Understanding

IEEE Transactions on Affective Computing
In the era of large language models (LLMs), tasks associated with “System I” cognition—those that are fast, automatic, and intuitive, such as sentiment analysis and text classification—are often considered effectively solved.
Yazhou Zhang, Chunwang Zou, Zheng Lian, Prayag Tiwari, Jing Qin   +4 more
semanticscholar   +1 more source