Deep Learning Approaches for Sarcasm Detection in Textual Data: A Comparative Study of BERT and EmoBERT

The identification of sarcasm in natural language processing is still a difficult task since it depends on subtle emotional and contextual cues. This study compares two cutting-edge transformer-based models, BERT and EmoBERT, to detect sarcasm in textual data. Although EmoBERT incorporates emotion-specific representations, EmoBERT improves upon BERT's well-known general-purpose language understanding capabilities. Both models are refined and assessed on parameters like accuracy, precision, recall, and F1-score using a benchmark dataset labelled with sarcasm. The findings show that EmoBERT performs better on sarcasm identification tasks because it can more accurately capture the underlying emotions that are frequently essential for interpreting sarcastic statements. This study provides insights into the effectiveness of emotion-aware language models and lays the basis for further research in context-sensitive sentiment analysis. The capacity of EmoBERT to recognize sarcastic phrases with implicit emotional contrast, like extreme optimism in unfavourable contexts, is stronger. By adding emotion embeddings, EmoBERT can more accurately identify sentiment polarity mismatches, which are a crucial aspect of sarcasm.