Sentiment as a prior for movie rating prediction

Abstract

Movie ratings play an important role in tasks such as user movie recommendations, verifying the relationship between user-submitted reviews and ratings etc. The ability to predict the rating of a movie would be useful considering these aspects. In this work, firstly, we propose methods to predict the movie rating based on its summary. We then set out to use priors that are generally available with movie summaries in order to improve the accuracy. In order to achieve this, we consider the associated movie reviews as well while predicting the rating and provide insights on why this helps our models perform better. We use the review based sentiment along with the summary in order to predict the rating more accurately since the sentiment captures a lot of essential information that can aid rating prediction. We experiment with various deep learning architectures and the results show a significant accuracy boost of around 2% in most of the models which show the generalizability of our approach.