Enhancing Sarcasm Detection Using Deep Learning

This study proposed various ways of sarcasm detection using machine learning and NLP methodologies. The model’s performance is tested on several datasets, using the models of deep learning, including RNN and LSTM, and classic models like SVM. We have also developed an innovative ensemble model combining a step decay learning rate scheduler, RNN, LSTM, and SVM. Incorporating attention mechanisms allowing this model achieved an astonishing 98.68% accuracy along with increased interpretability. The study has illustrated application in sentiment analysis in this novel way—the learning rate schedule further improves the model’s flexibility and persistence.