Classification of Medical Text using ML and DL Techniques

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The use of sarcasm in everyday conversation has recently risen to prominence. All the kids of our age utilise sarcasm to convey a negative message in a more nuanced manner. With the advancement of AI and machine learning techniques in the area of natural language processing (NLP), it has become more difficult to reliably and effectively identify sarcasm. This research provides a new method for sarcasm detection using machine studying and deep learning, hoping to make a meaningful contribution to this expanding area of study. In order to prepare the phrase for a hybrid deep learning model for training and classification, this method employs bidirectional encoder representations from transformers (BERT). The combination of CNN and LSTM creates the hybrid model used here. The suggested model has been tested on two datasets, with the goal of identifying sarcasm from nonsarcastic words. The trained model obtained a 99.63% accuracy rate, a 99.33% precision rate, a 99.83% recall rate, and an F1-score of 99.56%. These findings are based on 10 rounds of crossvalidation performed on the model that was suggested using the medical datasets.