Amazon Food Review Classification using Deep Learning and Recommender System

In this paper we implemented different models to solve the review usefulness classification problem. Both feed-forward neural network and LSTM were able to beat the baseline model. Performances of the models are evaluated using 0-1 loss and F-1 scores. In general, LSTM outperformed feed-forward neural network, as we trained our own word vectors in that model, and LSTM itself was able to store more information as it processes sequence of words. Besides, we built a recommender system using the user-item-rating data to further investigate this dataset and intended to make connection with review classification. The performance of recommender system is measured by RMSE in rating predictions.