NAIVE BAYES BINARY CLASSIFICATION FOR FILM REVIEW
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Online streaming services provide thousands of movie collections that can be watched by customers, viewers usually choose films based on reviews and ratings. To save time in the selection of good programs, there needs to be a tool to classify the various reviews available to choose which films are worth watching. Classification is an important topic in machine learning and data mining. In this study we use the naïve bayes algorithm which is one of the most efficient and effective algorithms for classification. The data set used in this study is the IMDB film review data taken from , as many as 50000 data that has been given 2 types of classification labels, namely "negative" and "positive".It can be concluded that the accuracy of the Naïve Bayes algorithm by applying the Multinomial and Beroulli methods is able to classify film reviews well in all test scenarios with the best accuracy achieved is 84%.