Enhanced Sarcasm and Emotion Detection Through Unified Model of Transformer and FCNets

Abstract

In this study, we present a unified model for efficient emotion and sarcasm identification in many languages. We accomplish resilient performance by integrating hybrid transformers and fully connected networks (FCNets) in our methodology. Extracting datasets in several languages, doing exploratory data analysis, preprocessing, extracting features, training and testing models, and finally deploying the system are all essential processes in the suggested strategy. Using a variety of models throughout training, we apply deep learning approaches to the problem of sarcasm and emotion recognition. The Flask framework chooses the model with the best performance to deploy. When applied to situations involving many languages, our method shows to be far more efficient and effective at identifying instances of sarcasm and emotion. Our model gives accuracy of 93.130 % for identifying sarcasm and emotion.