Sarcasm Detection
63 papers with code • 9 benchmarks • 14 datasets
The goal of Sarcasm Detection is to determine whether a sentence is sarcastic or non-sarcastic. Sarcasm is a type of phenomenon with specific perlocutionary effects on the hearer, such as to break their pattern of expectation. Consequently, correct understanding of sarcasm often requires a deep understanding of multiple sources of information, including the utterance, the conversational context, and, frequently some real world facts.
Benchmarks
These leaderboards are used to track progress in Sarcasm Detection
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Libraries
Use these libraries to find Sarcasm Detection models and implementationsDatasets
Reddit
BIG-bench
SARC
iSarcasm
MUStARD++
Sarcasm Corpus V2
WITS
Most implemented papers
UTNLP at SemEval-2022 Task 6: A Comparative Analysis of Sarcasm Detection Using Generative-based and Mutation-based Data Augmentation
amirabaskohi/semeval2022-task6-sarcasm-detection
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SemEval (NAACL) 2022
Using RoBERTa and mutation-based data augmentation, our best approach achieved an F1-sarcastic of 0. 38 in the competition's evaluation phase.
Tweet Sarcasm Detection Using Deep Neural Network
We investigate the use of neural network for tweet sarcasm detection, and compare the effects of the continuous automatic features with discrete manual features.
Magnets for Sarcasm: Making Sarcasm Detection Timely, Contextual and Very Personal
AniSkywalker/SarcasmDetection
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EMNLP 2017
Sarcasm is a pervasive phenomenon in social media, permitting the concise communication of meaning, affect and attitude.
CASCADE: Contextual Sarcasm Detection in Online Discussion Forums
SenticNet/CASCADE--ContextuAl-SarCAsm-DEtector
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COLING 2018
The literature in automated sarcasm detection has mainly focused on lexical, syntactic and semantic-level analysis of text.
Representing Social Media Users for Sarcasm Detection
kolchinski/reddit-sarc
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EMNLP 2018
We explore two methods for representing authors in the context of textual sarcasm detection: a Bayesian approach that directly represents authors' propensities to be sarcastic, and a dense embedding approach that can learn interactions between the author and the text.
Effectiveness of Data-Driven Induction of Semantic Spaces and Traditional Classifiers for Sarcasm Detection
Irony and sarcasm are two complex linguistic phenomena that are widely used in everyday language and especially over the social media, but they represent two serious issues for automated text understanding.
Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)
As a first step towards enabling the development of multimodal approaches for sarcasm detection, we propose a new sarcasm dataset, Multimodal Sarcasm Detection Dataset (MUStARD), compiled from popular TV shows.
Towards Multimodal Sarcasm Detection (An \_Obviously\_ Perfect Paper)
soujanyaporia/MUStARD
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ACL 2019
As a first step towards enabling the development of multimodal approaches for sarcasm detection, we propose a new sarcasm dataset, Multimodal Sarcasm Detection Dataset (MUStARD), compiled from popular TV shows.
