Efficient Word2Vec Vectors for Sentiment Analysis to Improve Commercial Movie Success

摘要

The results for predicting the gross for the IMDB data are presented here. The best possible classifier, i.e., random forest classifier, is used for gross prediction. Then sentiment analysis on the user reviews is done and sentiment polarity classification is obtained using various classifiers, along with the accuracies provided for each of them, and their running times. Along with this, our approach is discussed, and accuracies obtained using that approach were comparable to doc2vec approach. Our technique is also tested on Pang and Lee's dataset, and the accuracies are presented. Better accuracy with less time complexity and less space complexity was obtained. It is tentatively concluded that producer can thus change his marketing strategies according to the user reviews and thus increase the profit for the movie and as a result the gross. The future work includes integrating reviews from all social media possible, like Twitter, YouTube, etc. Also, the model can be extended to distributed systems.