DeepSarcasm: A BiLSTM and GloVe Powered Model for Identifying Sarcasm in Context

This project explores the intricate challenge of sarcasm detection in textual data using advanced Natural Language Processing (NLP) techniques. The primary goal is to create a model capable of accurately identifying and classifying sarcastic remarks within various contexts. We address this by leveraging Bidirectional Long Short-Term Memory (BiLSTM) networks, known for their ability to understand context by processing data in both forward and backward directions. To enhance semantic understanding, GloVe embeddings are employed to capture word relationships and contextual nuances. Our methodology encompasses comprehensive data preprocessing steps - such as tokenization, stopword removal, and lemmatization - to ensure clean and coherent text input. The BiLSTM model is trained on a diverse dataset that includes both sarcastic and non-sarcastic text samples, facilitating the learning of distinctive patterns. We evaluate the model’s performance using metrics like accuracy, precision, recall, and F1-score, anticipating that our model will effectively discern subtle sarcastic cues and outperform baseline methods. This study’s results have significant implications for sentiment analysis, social media monitoring, and conversational AI systems. Future directions include extending the model to handle multilingual sarcasm detection, integrating real-time data processing, and addressing ethical considerations in practical applications.