🧑‍🔬 Who cites Florian Kunneman

52 citations of their works from 52 papers of our library (of the 149,439 whose bibliographies we read) · 41 with a page here, the most cited first · ← Florian Kunneman

1. Sarcasm as Contrast between a Positive Sentiment and Negative SituationEllen Riloff, Ashequl Qadir, Prafulla Surve, et al.2013 · 754 citations
2. SemEval-2014 Task 9: Sentiment Analysis in TwitterInternational Workshop on Semantic Evaluation (SemEval)Sara Rosenthal, Alan Ritter, Preslav Nakov, et al.2014 · 741 citations
3. SemEval-2015 Task 10: Sentiment Analysis in TwitterInternational Workshop on Semantic Evaluation (SemEval)Sara Rosenthal, Preslav Nakov, Svetlana Kiritchenko, et al.2015 · 492 citations
4. Who cares about Sarcastic Tweets? Investigating the Impact of Sarcasm on Sentiment Analysis.Language Resources and Evaluation Conference (LREC)Diana Maynard, Mark Greenwood2014 · 437 citations
5. Harnessing Context Incongruity for Sarcasm DetectionAnnual Meeting of the Association for Computational Linguistics (ACL)Aditya Joshi, Vinita Sharma, Pushpak Bhattacharyya2015 · 417 citations
6. Fracking Sarcasm using Neural NetworkWorkshop on Computational Approaches to Subjectivity, Sentiment and Social Media AnalysisAniruddha Ghosh, Tony Veale2016 · 350 citations
7. A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural NetworksInternational Conference on Computational Linguistics (COLING)Soujanya Poria, Erik Cambria, Devamanyu Hazarika, et al.2016 · 335 citations
8. Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)Annual Meeting of the Association for Computational Linguistics (ACL)Santiago Castro, Devamanyu Hazarika, Verónica Pérez-Rosas, et al.2019 · 320 citations
9. Towards Multimodal Sarcasm Detection (An \textitObviously Perfect Paper)Annual Meeting of the Association for Computational Linguistics (ACL)Santiago Castro, Devamanyu Hazarika, Verónica Pérez-Rosas, et al.2019 · 228 citations
10. Modelling Sarcasm in Twitter, a Novel ApproachWorkshop on Computational Approaches to Subjectivity, Sentiment and Social Media AnalysisFrancesco Barbieri, Horacio Saggion, Francesco Ronzano2014 · 218 citations
11. Automatic Sarcasm Detection: A SurveyarXiv:1602.03426Aditya Joshi, Pushpak Bhattacharyya, Mark James Carman2016 · 216 citations
12. Are Word Embedding-based Features Useful for Sarcasm Detection?Conference on Empirical Methods in Natural Language Processing (EMNLP)Aditya Joshi, Vaibhav Tripathi, Kevin Patel, et al.2016 · 212 citations
13. Magnets for Sarcasm: Making Sarcasm Detection Timely, Contextual and Very PersonalConference on Empirical Methods in Natural Language Processing (EMNLP)Aniruddha Ghosh, Tony Veale2017 · 175 citations
14. Sarcastic or Not: Word Embeddings to Predict the Literal or Sarcastic Meaning of WordsConference on Empirical Methods in Natural Language Processing (EMNLP)Debanjan Ghosh, Weiwei Guo, Smaranda Muresan2015 · 142 citations
15. Learning Cognitive Features from Gaze Data for Sentiment and Sarcasm Classification using Convolutional Neural NetworkAnnual Meeting of the Association for Computational Linguistics (ACL)Abhijit Mishra, Kuntal Dey, Pushpak Bhattacharyya2017 · 141 citations
16. Sarcasm Analysis Using Conversation ContextComputational LinguisticsDebanjan Ghosh, Alexander R. Fabbri, Smaranda Muresan2018 · 119 citations
17. Evaluative Language Beyond Bags of Words: Linguistic Insights and Computational ApplicationsComputational LinguisticsFarah Benamara, Maite Taboada, Yannick Mathieu2017 · 104 citations
18. Modelling Irony in TwitterStudent Research Workshop at the Conference of the European Chapter of the Association for Computational LinguisticsFrancesco Barbieri, Horacio Saggion2014 · 104 citations
19. A Report on the 2020 Sarcasm Detection Shared TaskWorkshop on Figurative Language ProcessingDebanjan Ghosh, Avijit Vajpayee, Smaranda Muresan2020 · 92 citations
20. Harnessing Cognitive Features for Sarcasm DetectionAnnual Meeting of the Association for Computational Linguistics (ACL)Abhijit Mishra, Diptesh Kanojia, Seema Nagar, et al.2016 · 85 citations
21. Towards a Contextual Pragmatic Model to Detect Irony in TweetsAnnual Meeting of the Association for Computational Linguistics (ACL)Jihen Karoui, Farah Benamara Zitoune, Véronique Moriceau, et al.2015 · 81 citations
22. NTUA-SLP at SemEval-2018 Task 3: Tracking Ironic Tweets using Ensembles of Word and Character Level Attentive RNNsInternational Workshop on Semantic Evaluation (SemEval)Christos Baziotis, Athanasiou Nikolaos, Pinelopi Papalampidi, et al.2018 · 74 citations
23. Are you serious?: Rhetorical Questions and Sarcasm in Social Media DialogShereen Oraby, Vrindavan Harrison, Amita Misra, et al.2017 · 70 citations
24. A Novel Hierarchical BERT Architecture for Sarcasm DetectionWorkshop on Figurative Language ProcessingHimani Srivastava, Vaibhav Varshney, Surabhi Kumari, et al.2020 · 62 citations
25. Building a Bridge: A Method for Image-Text Sarcasm Detection Without Pretraining on Image-Text DataProceedings of the First International Workshop on Natural Language Processing Beyond TextXinyu Wang, Xiaowen Sun, Tan Yang, et al.2020 · 62 citations
26. iSarcasm: A Dataset of Intended SarcasmSilviu Oprea, Walid Magdy2020 · 58 citations
27. Modelling Irony in Twitter: Feature Analysis and EvaluationLanguage Resources and Evaluation Conference (LREC)Francesco Barbieri, Horacio Saggion2014 · 42 citations
28. We Usually Don't Like Going to the Dentist: Using Common Sense to Detect Irony on TwitterCynthia Van Hee, Els Lefever, Véronique Hoste2018 · 39 citations
29. Reactive Supervision: A New Method for Collecting Sarcasm DataConference on Empirical Methods in Natural Language Processing (EMNLP)Boaz Shmueli, Lun-Wei Ku, Soumya Ray2020 · 36 citations
30. Chinese Irony Corpus Construction and Ironic Structure AnalysisInternational Conference on Computational Linguistics (COLING)Yi-jie Tang, Hsin-Hsi Chen2014 · 33 citations
31. “When Numbers Matter!”: Detecting Sarcasm in Numerical Portions of TextWorkshop on Computational Approaches to Subjectivity, Sentiment and Social Media AnalysisAbhijeet Dubey, Lakshya Kumar, Arpan Somani, et al.2019 · 29 citations
32. Detecting Sarcasm is Extremely Easy ;-)Workshop on Computational Semantics beyond Events and RolesNatalie Parde, Rodney Nielsen2018 · 24 citations
33. A Survey on Automated Sarcasm Detection on TwitterarXiv:2202.02516Bleau Moores, Vijay Mago2022 · 19 citations
34. LLT-PolyU: Identifying Sentiment Intensity in Ironic TweetsInternational Workshop on Semantic Evaluation (SemEval)Hongzhi Xu, Enrico Santus, Anna Laszlo, et al.2015 · 19 citations
35. Wait Signals Predict Sarcasm in Online DebatesDialogue & DiscourseJ. Trevor D'Arcey, Shereen Oraby, Jean E. Fox Tree2019 · 14 citations
36. Interpreting Verbal Irony: Linguistic Strategies and the Connection to the Type of Semantic IncongruityDebanjan Ghosh, Elena Musi, Smaranda Muresan2020 · 10 citations
37. Sarcasm Detection in a Disaster ContextLanguage Resources and Evaluation Conference (LREC)Tiberiu Sosea, Junyi Jessy Li, Cornelia Caragea2024 · 8 citations
38. ‘Who would have thought of that!': A Hierarchical Topic Model for Extraction of Sarcasm-prevalent Topics and Sarcasm DetectionWorkshop on Extra-Propositional Aspects of Meaning in Computational LinguisticsAditya Joshi, Prayas Jain, Pushpak Bhattacharyya, et al.2016 · 5 citations
39. Computational Sarcasm Analysis on Social Media: A Systematic ReviewarXiv:2209.06170Faria Binte Kader, Nafisa Hossain Nujat, Tasmia Binte Sogir, et al.2022 · 4 citations
40. UWB at SemEval-2018 Task 3: Irony detection in English tweetsInternational Workshop on Semantic Evaluation (SemEval)Tomáš Hercig2018 · 1 citation
41. Linguistic Features in Automatic Sarcasm DetectionMartina Ducret, Lauren Kruse, Carlos Martinez, et al.2020

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