A BERT-Based with Fuzzy logic Sentimental Classifier for Sarcasm Detection

This study introduces a BERT-based approach with fuzzy logic for sarcasm detection in natural language processing (NLP). The research aims to capture the subtle indicators of sarcasm by utilizing advanced models such as Fuzzy logic, Transformer, BERT, and RNN. The incorporation of fuzzy logic enhances the precision of detection by interpreting the subtleties present in sarcastic expressions and accommodating ambiguity and context dependencies. The methodology involves collecting a diverse dataset of comments and applying iterative learning using Transformer, BERT, and RNN models. The integration of fuzzy logic post-iteration further refines the model's ability to identify sarcastic nuances in a context-aware manner. This research demonstrates the efficiency of iterative learning and fuzzy logic techniques for sarcasm detection and presents an innovative approach that opens doors for more reliable natural language understanding systems.