Implementation of Sentiment Classification of Movie Reviews by Supervised Machine Learning Approaches

Entertainment is crucial part of human life entertainments like songs, music, drama and movies etc. So for watching good movies most of the peoples generally prefer theater. If movie is not good then we feel nervous and we think wasted our money and time for watching bad movie so people prefer to go to movie by reading reviews and rating for that movie on various apps like IMDb, flixter and voice etc. But by reading one or two reviews we cannot say movie is good or bad because different peoples have different opinions some peoples like action or some like thrill or romance so people gives reviews based on their area of interest. So we cannot predict movie for that we proposed movie reviews based on sentiment analysis and classification algorithms such as Naïve bayes and Random forest (RF). Sentiment analysis generally utilized to identify the sentiment of huge amount of text. We compare naïve bayes and RF machine learning techniques for measuring negative, positive and neutral reviews.