Detecting sarcasm is one of the more challenging tasks in NLP because the intended meaning is often the opposite of the literal meaning. There isn’t a single technique that works best in every scenario, but modern transformer-based models generally outperform traditional NLP methods.

Some common approaches include:

• Transformer Models (Best Overall): Models like BERT, RoBERTa, and DeBERTa capture context and semantic relationships, making them highly effective for sarcasm detection. Fine-tuning these models on labeled sarcasm datasets typically produces the best results.
• Traditional Machine Learning: Algorithms such as Support Vector Machines (SVM), Logistic Regression, or Random Forests using features like TF-IDF or n-grams can work reasonably well on smaller datasets. However, they often struggle to capture contextual meaning and irony.
• Sentiment + Context Analysis: Sarcasm frequently contains a mismatch between sentiment and context. For example, “Great job breaking the production server!” uses positive words in a clearly negative situation. Models that combine sentiment analysis with contextual understanding tend to perform better than sentiment analysis alone.
• Conversation Context: Many sarcastic statements only make sense when previous messages are considered. Incorporating surrounding dialogue or conversational history can significantly improve detection accuracy.

Example using Hugging Face Transformers

from transformers import pipeline

classifier = pipeline(
    "text-classification",
    model="cardiffnlp/twitter-roberta-base-sentiment-latest"
)

text = "Oh great, another Monday morning meeting."

result = classifier(text)
print(result)

For a dedicated sarcasm classifier, you would fine-tune a transformer model on a labeled sarcasm dataset (such as those from SemEval or Twitter sarcasm datasets) rather than using a general sentiment model.