A Smart Movie Suitability Rating System Based on Subtitle

With the enormous growth rate in the number of movies coming into our lives, it can be very challenging to decide whether a movie is suitable for a family or not. Almost every country has a Movie Rating System that determines movies’ suitability age. But these current movie rating systems require watching the full movie with a professional. In this paper, we developed a model which can determine the rating level of the movie by only using its subtitle without any professional interfere. To convert the text data to numbers, we use TF-IDF vectorizer, WIDF vectorizer and Glasgow Weighting Scheme. We utilized random forest, support vector machine, k-nearest neighbor and multinomial naive bayes to find the best combination that achieves the highest results. We achieved an accuracy of 85%. The result of our classification approach is promising and can be used by the movie rating committee for pre-evaluation. Cautionary Note: In some chapters of this paper may contain some words that many will find offensive or inappropriateness; however, this cannot be avoided owing to the nature of the work

Keywords:

machine learning, deep learning, natural language processing, nlp, subtitles, movie ratings parental guidelines,

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