Sarcasm Detection
64 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
Latest papers with no code
Retrofitting Light-weight Language Models for Emotions using Supervised Contrastive Learning
We present a novel retrofitting method to induce emotion aspects into pre-trained language models (PLMs) such as BERT and RoBERTa.
An Evaluation of State-of-the-Art Large Language Models for Sarcasm Detection
Sarcasm, as defined by Merriam-Webster, is the use of words by someone who means the opposite of what he is trying to say.
Sarcasm in Sight and Sound: Benchmarking and Expansion to Improve Multimodal Sarcasm Detection
The introduction of the MUStARD dataset, and its emotion recognition extension MUStARD++, have identified sarcasm to be a multi-modal phenomenon -- expressed not only in natural language text, but also through manners of speech (like tonality and intonation) and visual cues (facial expression).
BNS-Net: A Dual-channel Sarcasm Detection Method Considering Behavior-level and Sentence-level Conflicts
Sarcasm detection is a binary classification task that aims to determine whether a given utterance is sarcastic.
A Wide Evaluation of ChatGPT on Affective Computing Tasks
In this work, we widely study the capabilities of the ChatGPT models, namely GPT-4 and GPT-3. 5, on 13 affective computing problems, namely aspect extraction, aspect polarity classification, opinion extraction, sentiment analysis, sentiment intensity ranking, emotions intensity ranking, suicide tendency detection, toxicity detection, well-being assessment, engagement measurement, personality assessment, sarcasm detection, and subjectivity detection.
Sarcasm Detection in a Disaster Context
This contempt is in some cases expressed as sarcasm or irony.
Generating Faithful Synthetic Data with Large Language Models: A Case Study in Computational Social Science
Large Language Models (LLMs) have democratized synthetic data generation, which in turn has the potential to simplify and broaden a wide gamut of NLP tasks.
Researchers eye-view of sarcasm detection in social media textual content
The enormous use of sarcastic text in all forms of communication in social media will have a physiological effect on target users.
Polarity based Sarcasm Detection using Semigraph
The proposed method is to obtain the sarcastic and non-sarcastic polarity scores of a document using a semigraph.
Thematic context vector association based on event uncertainty for Twitter
The extraction of keywords with respective contextual events in Twitter data is a big challenge.
