Sarcasm Detection in News Headlines Using Bidirectional LSTM

Abstract

This project explores sarcasm detection in news headlines using a Bidirectional LSTM model. It involves preprocessing text data, embedding words with TensorFlow, and training a deep learning model to classify whether a headline is sarcastic or not. The notebook includes data cleaning, model architecture, training process, and evaluation metrics.