🔗 Who cites «Humans Require Context to Infer Ironic Intent (so Computers Probably do, too)»

61 papers of our library cite it (of the 141,840 whose bibliographies we read) · 37 with a page here, the most cited first · ← back to «Humans Require Context to Infer Ironic Intent (so Computers Probably do, too)»

1. Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)Santiago Castro, Devamanyu Hazarika, Verónica Pérez-Rosas, et al.2019 · 314 citations
2. A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence ClassificationYe Zhang, Byron Wallace2017 · 259 citations
3. A Large Self-Annotated Corpus for SarcasmMikhail Khodak, Nikunj Saunshi, Kiran Vodrahalli2018 · 258 citations
4. The Importance of Modeling Social Factors of Language: Theory and PracticeDirk Hovy, Diyi Yang2021 · 234 citations
5. Towards Multimodal Sarcasm Detection (An \textitObviously Perfect Paper)Santiago Castro, Devamanyu Hazarika, Verónica Pérez-Rosas, et al.2019 · 228 citations
6. Automatic Sarcasm Detection: A SurveyAditya Joshi, Pushpak Bhattacharyya, Mark James Carman2016 · 216 citations
7. Are Word Embedding-based Features Useful for Sarcasm Detection?Aditya Joshi, Vaibhav Tripathi, Kevin Patel, et al.2016 · 212 citations
8. Creating and Characterizing a Diverse Corpus of Sarcasm in DialogueShereen Oraby, Vrindavan Harrison, Lena Reed, et al.2016 · 163 citations
9. Sparse, Contextually Informed Models for Irony Detection: Exploiting User Communities, Entities and SentimentByron C. Wallace, Do Kook Choe, Eugene Charniak2015 · 123 citations
10. Sarcasm Analysis Using Conversation ContextDebanjan Ghosh, Alexander R. Fabbri, Smaranda Muresan2018 · 119 citations
11. A Report on the 2020 Sarcasm Detection Shared TaskDebanjan Ghosh, Avijit Vajpayee, Smaranda Muresan2020 · 92 citations
12. CASCADE: Contextual Sarcasm Detection in Online Discussion ForumsDevamanyu Hazarika, Soujanya Poria, Sruthi Gorantla, et al.2018 · 89 citations
13. Harnessing Sequence Labeling for Sarcasm Detection in Dialogue from TV Series ‘Friends'Aditya Joshi, Vaibhav Tripathi, Pushpak Bhattacharyya, et al.2016 · 87 citations
14. Putting Sarcasm Detection into Context: The Effects of Class Imbalance and Manual Labelling on Supervised Machine Classification of Twitter ConversationsGavin Abercrombie, Dirk Hovy2016 · 85 citations
15. Sarcasm Detection Using an Ensemble ApproachJens Lemmens, Ben Burtenshaw, Ehsan Lotfi, et al.2020 · 56 citations
16. Representing Social Media Users for Sarcasm DetectionY. Alex Kolchinski, Christopher Potts2018 · 54 citations
17. Detecting Sarcasm in Conversation Context Using Transformer-Based ModelsAdithya Avvaru, Sanath Vobilisetty, Radhika Mamidi2020 · 46 citations
18. WLV at SemEval-2018 Task 3: Dissecting Tweets in Search of IronyOmid Rohanian, Shiva Taslimipoor, Richard Evans, et al.2018 · 39 citations
19. C-Net: Contextual Network for Sarcasm DetectionAmit Kumar Jena, Aman Sinha, Rohit Agarwal2020 · 38 citations
20. Breaking NLP: Using Morphosyntax, Semantics, Pragmatics and World Knowledge to Fool Sentiment Analysis SystemsTaylor Mahler, Willy Cheung, Micha Elsner, et al.2017 · 34 citations
21. Neural Sarcasm Detection using Conversation ContextNikhil Jaiswal2020 · 32 citations
22. A deep-learning framework to detect sarcasm targetsJasabanta Patro, Srijan Bansal, Animesh Mukherjee2019 · 29 citations
23. Latent-Optimized Adversarial Neural Transfer for Sarcasm DetectionXu Guo, Boyang Li, Han Yu, et al.2021 · 27 citations
24. A Survey on Automated Sarcasm Detection on TwitterBleau Moores, Vijay Mago2022 · 19 citations
25. The Iron(ic) Melting Pot: Reviewing Human Evaluation in Humour, Irony and Sarcasm GenerationTyler Loakman, Aaron Maladry, Chenghua Lin2023 · 18 citations
26. Perceived and Intended Sarcasm Detection with Graph Attention NetworksJoan Plepi, Lucie Flek2021 · 18 citations
27. Does Commonsense help in detecting Sarcasm?Somnath Basu Roy Chowdhury, Snigdha Chaturvedi2021 · 17 citations
28. A Survey in Automatic Irony Processing: Linguistic, Cognitive, and Multi-X PerspectivesQingcheng Zeng, An-Ran Li2022 · 14 citations
29. Sarcasm Detection is Way Too Easy! An Empirical Comparison of Human and Machine Sarcasm DetectionIbrahim Abu Farha, Steven Wilson, Silviu Oprea, et al.2022 · 13 citations
30. Perceived and Intended Sarcasm Detection with Graph Attention NetworksJoan Plepi, Lucie Flek2021 · 11 citations
31. Interpreting Verbal Irony: Linguistic Strategies and the Connection to the Type of Semantic IncongruityDebanjan Ghosh, Elena Musi, Smaranda Muresan2020 · 10 citations
32. How Challenging is Sarcasm versus Irony Classification?: A Study With a Dataset from English LiteratureAditya Joshi, Vaibhav Tripathi, Pushpak Bhattacharyya, et al.2016 · 10 citations
33. NULL at SemEval-2022 Task 6: Intended Sarcasm Detection Using Stylistically Fused Contextualized Representation and Deep LearningMostafa Rahgouy, Hamed Babaei Giglou, Taher Rahgooy, et al.2022 · 6 citations
34. ‘Who would have thought of that!': A Hierarchical Topic Model for Extraction of Sarcasm-prevalent Topics and Sarcasm DetectionAditya Joshi, Prayas Jain, Pushpak Bhattacharyya, et al.2016 · 5 citations
35. Computational Sarcasm Analysis on Social Media: A Systematic ReviewFaria Binte Kader, Nafisa Hossain Nujat, Tasmia Binte Sogir, et al.2022 · 4 citations
36. I2C at SemEval-2022 Task 6: Intended Sarcasm in English using Deep Learning TechniquesAdrián Moreno Monterde, Laura Vázquez Ramos, Jacinto Mata, et al.2022 · 3 citations
37. Is AI Catching Up to Human Expression? Exploring Emotion, Personality, Authorship, and Linguistic Style in English and Arabic with Six Large Language ModelsNasser A Alsadhan2026

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