Exploring Sarcasm Detection: Leveraging Neural Network Models with BERT Embeddings

Sarcasm is a form of verbal mockery, which poses a unique challenge for common people to understand, this also leads to difficulties in Natural Language Processing and sentiment analysis. This study deals with Sarcasm detection from textual data and a few machine learning and ensemble models were compared with the proposed Deep Learning architecture. BERT was used for the extraction of features and vectorization of textual data. The study utilizes curated datasets consisting of non-sarcastic and sarcastic samples sourced from Kaggle and Reddit platforms. The models will be evaluated using standard metrics. The best ML model accuracy achieved was 0.88 and an F1 score of 0.89 with a combination of Bert Embeddings and the SVM model. The proposed methodology using BERT embeddings a simple neural architecture achieved an accuracy of 0.92 on the test dataset with a F1-score of 0.91. It has a far lesser complexity than other well-known Deep learning architectures in use in other works. With the live streaming of The proposed Neural Network model achieved an accuracy of 0.94 and an F measure of 0.90 on the data streamed from Reddit.