A BERT-based with fuzzy logic sentimental classifier for sarcasm detection
Tianyou Wang · Applied and Computational Engineering · 2024
This study explores the challenging task of sarcasm detection in text, a crucial aspect of sentiment analysis in Natural Language Processing (NLP). Sarcasm, characterized by irony and nuanced language, often complicates the interpretation of emotional tone in text. To address this, the study employs a hybrid model that integrates BERT (Bidirectional Encoder Representations from Transformers) with fuzzy logic. BERT's deep semantic understanding is combined with the sequential processing of LSTM and the flexible decision-making capabilities of fuzzy logic. The model's performance is validated using multiple datasets, demonstrating a significant improvement in accuracy, particularly in detecting subtle and context-dependent sarcasm. This research contributes to the advancement of sentiment analysis, offering a robust framework for handling complex linguistic expressions in various NLP applications.