Wu, Y.[Yang],
Zhao, Y.Y.[Yan-Yan],
Lu, X.[Xin],
Qin, B.[Bing],
Wu, Y.[Yin],
Sheng, J.[Jian],
Li, J.L.[Jin-Long],
Modeling Incongruity between Modalities for Multimodal Sarcasm
Detection,
MultMedMag(28), No. 2, April 2021, pp. 86-95.
IEEE DOI
2107
Feature extraction, Acoustics, Visualization, Videos,
Sentiment analysis, Face recognition, Acoustic measurements
BibRef
Jain, D.K.[Deepak Kumar],
Kumar, A.[Akshi],
Sangwan, S.R.[Saurabh Raj],
TANA: The amalgam neural architecture for sarcasm detection in indian
indigenous language combining LSTM and SVM with word-emoji embeddings,
PRL(160), 2022, pp. 11-18.
Elsevier DOI
2208
Deep Learning, Sarcasm, LSTM, SVM, Hindi
BibRef
Bedi, M.[Manjot],
Kumar, S.[Shivani],
Akhtar, M.S.[Md Shad],
Chakraborty, T.[Tanmoy],
Multi-Modal Sarcasm Detection and Humor Classification in Code-Mixed
Conversations,
AffCom(14), No. 2, April 2023, pp. 1363-1375.
IEEE DOI
2306
Task analysis, Visualization, Semantics, Context modeling, Acoustics,
Switches, Planning, Sarcasm detection, humor classification,
multi-modality
BibRef
Sahu, G.A.[Geeta Abakash],
Hudnurkar, M.[Manoj],
Sarcasm Detection: A Review, Synthesis and Future Research Agenda,
IJIG(23), No. 6 2023, pp. 2350061.
DOI Link
2312
BibRef
Liu, Y.C.[Yao-Chen],
Zhang, Y.Z.[Ya-Zhou],
Song, D.W.[Da-Wei],
A Quantum Probability Driven Framework for Joint Multi-Modal Sarcasm,
Sentiment and Emotion Analysis,
AffCom(15), No. 1, January 2024, pp. 326-341.
IEEE DOI
2403
Emotion recognition, Task analysis, Sentiment analysis,
Correlation, Interference, Context modeling, Analytical models,
sentiment analysis
BibRef
Liang, B.[Bin],
Gui, L.[Lin],
He, Y.L.[Yu-Lan],
Cambria, E.[Erik],
Xu, R.F.[Rui-Feng],
Fusion and Discrimination: A Multimodal Graph Contrastive Learning
Framework for Multimodal Sarcasm Detection,
AffCom(15), No. 4, October 2024, pp. 1874-1888.
IEEE DOI
2412
Visualization, Feature extraction, Optical character recognition,
Self-supervised learning, Affective computing,
sarcasm detection
BibRef
Wei, Y.W.[Yi-Wei],
Zhou, H.Y.[Heng-Yang],
Yuan, S.[Shaozu],
Chen, M.[Meng],
Shi, H.T.[Hai-Tao],
Jia, Z.Y.[Zhi-Yang],
Wang, L.B.[Long-Biao],
He, X.D.[Xiao-Dong],
DeepMSD: Advancing Multimodal Sarcasm Detection Through
Knowledge-Augmented Graph Reasoning,
CirSysVideo(35), No. 7, July 2025, pp. 6413-6423.
IEEE DOI
2507
Cognition, Knowledge engineering, Image edge detection,
Visualization, Semantics, Noise, Context modeling,
cross-knowledge graph
BibRef
Bao, Y.T.[Yong-Tang],
Zhao, X.[Xin],
Zhang, P.[Peng],
Qi, Y.[Yue],
Li, H.J.[Hao-Jie],
HIAN: A hybrid interactive attention network for multimodal sarcasm
detection,
PR(164), 2025, pp. 111535.
Elsevier DOI
2504
Multimodal sarcasm, Multimodal sentiment, Attention mechanism,
Transformer, Deep learning
BibRef
Zhang, X.Q.[Xiao-Qiang],
Li, G.Y.[Guang-Yao],
Li, X.M.[Xiao-Meng],
Liang, B.[Buwen],
Chen, Y.[Ying],
Sarcasm detection enhanced by multi-modal topics using denoising
diffusion probabilistic models,
PR(171), 2026, pp. 112130.
Elsevier DOI
2510
Multi-modal sarcasm detection, Multi-modal topic modeling,
Diffusion model, Feature reconstruction
BibRef
Gao, X.[Xiyuan],
Nayak, S.[Shekhar],
Coler, M.[Matt],
Spoken in Jest, Detected in Earnest: A Systematic Review of Sarcasm
Recognition: Multimodal Fusion, Challenges, and Future Prospects,
AffCom(16), No. 4, October 2025, pp. 2526-2544.
IEEE DOI
2512
Speech recognition, Systematic literature review,
Feature extraction, Databases, Oral communication, Data mining,
prosody
BibRef
Zhang, Y.Z.[Ya-Zhou],
Zou, C.[Chunwang],
Lian, Z.[Zheng],
Tiwari, P.[Prayag],
Qin, J.[Jing],
SarcasmBench: Towards Evaluating Large Language Models on Sarcasm
Understanding,
AffCom(16), No. 4, October 2025, pp. 2560-2578.
IEEE DOI
2512
Benchmark testing, Affective computing, Large language models,
Sentiment analysis, Training, Text categorization,
prompting learning
BibRef
Yuan, S.[Shaozu],
Wei, Y.W.[Yi-Wei],
Zhou, H.Y.[Heng-Yang],
Xu, Q.[Qinfu],
Chen, M.[Meng],
He, X.D.[Xiao-Dong],
Enhancing Semantic Awareness by Sentimental Constraint With Automatic
Outlier Masking for Multimodal Sarcasm Detection,
MultMed(27), 2025, pp. 5376-5386.
IEEE DOI
2509
Semantics, Visualization, Training, Feature extraction, Correlation,
Robustness, Artificial intelligence, Anomaly detection,
automatic outlier masking
BibRef
Wen, C.S.[Chang-Song],
Jia, G.[Guoli],
Yang, J.F.[Ju-Feng],
DIP: Dual Incongruity Perceiving Network for Sarcasm Detection,
CVPR23(2540-2550)
IEEE DOI
2309
BibRef
Pramanick, S.[Shraman],
Roy, A.[Aniket],
Johns, V.M.P.[Vishal M. Patel],
Multimodal Learning using Optimal Transport for Sarcasm and Humor
Detection,
WACV22(546-556)
IEEE DOI
2202
Training, Learning systems, Training data,
Benchmark testing, Synchronization, Task analysis,
Vision Systems and Applications
BibRef
Zhang, L.[Lei],
Zhao, X.M.[Xiao-Ming],
Song, X.Q.[Xue-Qiang],
Fang, Y.W.[Yu-Wei],
Li, D.[Dong],
Wang, H.Z.[Hai-Zhou],
A Novel Chinese Sarcasm Detection Model Based on Retrospective Reader,
MMMod22(II:267-278).
Springer DOI
2203
BibRef
Alcaide, J.M.[José María],
Justo, R.[Raquel],
Torres, M.I.[María Inés],
Combining Statistical and Semantic Knowledge for Sarcasm Detection in
Online Dialogues,
IbPRIA15(662-671).
Springer DOI
1506
BibRef
Bharti, S.K.[Santosh Kumar],
Babu, K.S.[Korra Sathya],
Jena, S.K.[Sanjay Kumar],
Harnessing Online News for Sarcasm Detection in Hindi Tweets,
PReMI17(679-686).
Springer DOI
1711
BibRef
Chapter on OCR, Document Analysis and Character Recognition Systems continues in
Twitter Stream Analysis, Tweets, Texts, SMS, Internet .