  In this work, we predict the sentiment of restaurant reviews based on a
subset of the Yelp Open Dataset. We utilize the meta features and text
available in the dataset and evaluate several machine learning and
state-of-the-art deep learning approaches for the prediction task. Through
several qualitative experiments, we show the success of the deep models with
attention mechanism in learning a balanced model for reviews across different
restaurants. Finally, we propose a novel Multi-tasked joint BERT model that
improves the overall classification performance.
