A support vector machine mixed with statistical reasoning approach to predict movie success by analyzing public sentiments

Wisdom of Crowds is often considered a very powerful tool for predicting anything. In this paper we explore the power of public sentiments on predicting the success of movies. In short, we differentiated between positive and negative comments using Support Vector Machine and then use Statistical Reasoning to predict movie success. We used non linear RBF kernel for our sentiment classifier which achieved better accuracy than the classifiers that use linear kernels in the famous IMDB Movie Review Dataset (89.51% accuracy) and also in the Pang and Lee Movie Review Dataset (86.86% accuracy). Using our system we can predict whether a movie will be successful or not with an accuracy of 90.3%. We also compared our approach with other authors in the literature.