Sarcasm Detection Using LSTM, GRU

Natural Language Processing struggles to identify sarcasm because the intended meaning of a statement frequently differs from its literal interpretation. Context, tone, and cultural differences are needed to comprehend these phenomena, which frequently use comedy, irony, or satire. Sarcasm detection requires sophisticated linguistic and contextual knowledge, hence making it a demanding computational task. Accurate identification of sarcasm is important in applications such as sentiment analysis, interpretation of customer feedback, and monitoring of social media, where misperceptions may lead to inaccurate results thereby resulting in incorrect decision-making. This research study compares five sarcasm detection models, that span from traditional machine learning algorithms to recent deep learning models and transformer-based models. Every model is trained on a manually curated dataset from the Reddit platform, a website renowned for containing sarcastic and usually ambiguous user submissions. The testing process has taken into consideration the performance metrics accuracy, precision, recall, F1-score and examines how well every model comprehends context and semantic nuances. The aim of this analysis is to establish the model architectures that best addresses the inherent complexities of online text sarcasm detection.