NLP models struggle with sarcasm and irony because these linguistic phenomena often rely on tone, context, or shared cultural knowledge, which are not explicitly encoded in text. For example, the sentence "What a wonderful day!" can express genuine positivity or sarcasm, depending on the context.
Sentiment analysis models trained on literal interpretations of text often misclassify sarcastic statements. Addressing this requires specialized datasets that include sarcastic examples, as well as models designed to capture nuances in language. Transformer-based models like BERT or GPT improve sarcasm detection by leveraging context and relationships in text, but their success depends on the availability of high-quality, annotated sarcastic data.
Combining NLP with other modalities, such as tone or facial expression analysis, can enhance sarcasm detection in multimodal applications. Research is also exploring conversational history and user behavior to improve understanding of sarcasm in dialogues. While progress has been made, detecting sarcasm and irony remains a complex challenge for NLP systems.