🔗 Who cites «Sparse, Contextually Informed Models for Irony Detection: Exploiting User Communities, Entities and Sentiment»

32 papers of our library cite it (of the 141,840 whose bibliographies we read) · 22 with a page here, the most cited first · ← back to «Sparse, Contextually Informed Models for Irony Detection: Exploiting User Communities, Entities and Sentiment»

1. Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)Santiago Castro, Devamanyu Hazarika, Verónica Pérez-Rosas, et al.2019 · 314 citations
2. A Transformer-based approach to Irony and Sarcasm detectionRolandos Alexandros Potamias, Georgios Siolas, Andreas - Georgios Stafylopatis2019 · 312 citations
3. A Large Self-Annotated Corpus for SarcasmMikhail Khodak, Nikunj Saunshi, Kiran Vodrahalli2018 · 258 citations
4. Towards Multimodal Sarcasm Detection (An \textitObviously Perfect Paper)Santiago Castro, Devamanyu Hazarika, Verónica Pérez-Rosas, et al.2019 · 228 citations
5. Automatic Sarcasm Detection: A SurveyAditya Joshi, Pushpak Bhattacharyya, Mark James Carman2016 · 216 citations
6. Are Word Embedding-based Features Useful for Sarcasm Detection?Aditya Joshi, Vaibhav Tripathi, Kevin Patel, et al.2016 · 212 citations
7. Tweet Sarcasm Detection Using Deep Neural NetworkMeishan Zhang, Yue Zhang, Guohong Fu2016 · 190 citations
8. SemEval-2022 Task 6: iSarcasmEval, Intended Sarcasm Detection in English and ArabicIbrahim Abu Farha, Silviu Vlad Oprea, Steven Wilson, et al.2022 · 146 citations
9. Your Sentiment Precedes You: Using an author's historical tweets to predict sarcasmAnupam Khattri, Aditya Joshi, Pushpak Bhattacharyya, et al.2015 · 114 citations
10. CASCADE: Contextual Sarcasm Detection in Online Discussion ForumsDevamanyu Hazarika, Soujanya Poria, Sruthi Gorantla, et al.2018 · 89 citations
11. Harnessing Sequence Labeling for Sarcasm Detection in Dialogue from TV Series ‘Friends'Aditya Joshi, Vaibhav Tripathi, Pushpak Bhattacharyya, et al.2016 · 87 citations
12. Representing Social Media Users for Sarcasm DetectionY. Alex Kolchinski, Christopher Potts2018 · 54 citations
13. Exploring Author Context for Detecting Intended vs Perceived SarcasmSilviu Oprea, Walid Magdy2019 · 47 citations
14. NLPRL-IITBHU at SemEval-2018 Task 3: Combining Linguistic Features and Emoji pre-trained CNN for Irony Detection in TweetsHarsh Rangwani, Devang Kulshreshtha, Anil Kumar Singh2018 · 28 citations
15. 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
16. Should a Chatbot be Sarcastic? Understanding User Preferences Towards Sarcasm GenerationSilviu Vlad Oprea, Steven Wilson, Walid Magdy2022 · 13 citations
17. ValenTO at SemEval-2018 Task 3: Exploring the Role of Affective Content for Detecting Irony in English TweetsDelia Irazú Hernández Farías, Viviana Patti, Paolo Rosso2018 · 13 citations
18. R2D2 at SemEval-2022 Task 6: Are language models sarcastic enough? Finetuning pre-trained language models to identify sarcasmMayukh Sharma, Ilanthenral Kandasamy, Vasantha W B2022 · 2 citations
19. LDR at SemEval-2018 Task 3: A Low Dimensional Text Representation for Irony DetectionBilal Ghanem, Francisco Rangel, Paolo Rosso2018 · 2 citations
20. JCT at SemEval-2022 Task 6-A: Sarcasm Detection in Tweets Written in English and Arabic using Preprocessing Methods and Word N-gramsYaakov HaCohen-Kerner, Matan Fchima, Ilan Meyrowitsch2022 · 1 citation
21. Irony Detection in Hebrew Documents: A Novel Dataset and an Evaluation of Neural Classification MethodsAvi Shmidman, Elda Weizman, Avishay Gerczuk2025
22. Linguistic Features in Automatic Sarcasm DetectionMartina Ducret, Lauren Kruse, Carlos Martinez, et al.2020

10 more papers of our library cite it; they have no page here yet.

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/wallace-2015-sparse-contextually-informed-models-irony/cited-by/