Topluluk Öğrenme ile Google Uygulamalarının İçerik Derecelendirmelerini Analiz Etme

Year 2022, , 1038 - 1050, 30.09.2022

Ercan Atagün , Tunahan Timuçin , Serdar Biroğul

https://doi.org/10.31202/ecjse.1059822

Abstract

Android Market ismiyle piyasaya çıktıktan sonra Google Play ismiyle ününü tüm dünyaya duyuran, Google’ın Android kullanıcıları için geliştirdiği bir paket yöneticisi olan uygulama marketi, içerisinde birçok alana ve yaş aralığına hitap eden uygulamalar bulundurmaktadır. Uygulamaların yayıldığı geniş çerçeve ve “büyük veri” olarak adlandırılma seviyesine ulaşmış olan veri akışı, araştırmacıların dikkatini de çekmeye başlamıştır. Uygulama sayısındaki aşırı artış ebeveynlerin içerikler konusunda takibini zorlaştırmaktadır. Google Play üzerindeki uygulamaların içerik kontrolünün (content rating) sağlanabilmesi için makine öğrenmesi yöntemleri ile sınıflandırılmasına ihtiyaç duyulmaktadır. Bu çalışmada Google Play üzerindeki 10757 uygulamanın Category, Rating, Reviews, Size, Installs, Type, Genres, Last Updated, Current Version, Android Version özellikleri, Ensemble Learning yöntemleri (Adaboost, Bagging, Random Forest, Stacking), K-Nearest Neighbors, Logistic Regression ve Yapay Sinir Ağı algoritmaları ile analiz edilerek content rating sınıflandırılması yapılmıştır.

Keywords

topluluk öğrenme, sınıflandırma, içerik derecelendirme, google uygulamaları

Analysing Content Ratings of Google Apps with Ensemble Learning

Abstract

Google Play was launched under the name of Android Market and made its reputation known all over the world. The mobile application market, which is a package manager developed by Google for Android users, contains applications that appeal to many areas and age ranges. The wide area in which applications spread and the data flow, which has reached the level of being called “big data”, has started to attract the attention of researchers. The excessive increase in the number of applications makes it difficult for parents to follow up on the content. In order to provide content rating of applications on Google Play, it is needed to be classified by machine learning methods. In this study, content rating classification was made by analyzing “Category, Rating, Reviews, Size, Installs, Type, Genres, Last Updated, Current Version, Android Version” features of 10757 applications on Google Play, Ensemble Learning methods (Adaboost, Bagging, Random Forest, Stacking), Logistic Regression, Artificial Neural Network, K-Nearest Neighbors algorithms.

Keywords

Ensemble learning, classification, content rating

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