A Smart Movie Suitability Rating System Based on Subtitle

Abstract

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