| Yazarlar |
Dr. Öğr. Üyesi Murat IŞIK
Kırşehir Ahi Evran Üniversitesi, Türkiye |
| Özet |
| 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 |
| Anahtar Kelimeler |
| machine learning,deep learning,natural language processing,nlp,subtitles,movie ratings,parental guidelines |
| Makale Türü | Özgün Makale |
| Makale Alt Türü | SSCI, AHCI, SCI, SCI-Exp dergilerinde yayımlanan tam makale |
| Dergi Adı | Gazi Universitesi Fen Bilimleri Dergisi Part C: Tasarim ve Teknoloji |
| Dergi ISSN | 2147-9526 |
| Dergi Tarandığı Indeksler | AHCI |
| Dergi Grubu | Q4 |
| Makale Dili | İngilizce |
| Basım Tarihi | 03-2023 |
| Cilt No | 11 |
| Sayı | 1 |
| Sayfalar | 252 / 262 |
| Doi Numarası | 10.29109/gujsc.1146352 |
| Makale Linki | http://dx.doi.org/10.29109/gujsc.1146352 |
| Atıf Sayıları |
