Sarcastic Text

Sarcasm detection in text and multimodal data is a challenging area of natural language processing (NLP) research focused on accurately identifying the intended meaning behind sarcastic utterances, which often contradict their literal meaning. Current research employs various deep learning models, including transformers like BERT and RoBERTa, and explores the integration of multiple modalities (text, audio, visual) to better capture contextual cues and improve detection accuracy. Successful sarcasm detection has significant implications for sentiment analysis, improving the understanding of user opinions in social media, customer feedback, and other applications where nuanced language is prevalent. Furthermore, research is expanding to include the generation of sarcastic text and explanations of sarcasm, furthering our understanding of this complex linguistic phenomenon.