Akademik Veri Yönetim Sistemi
Mühendislik Bilimleri ve Tasarım Dergisi, cilt.9, sa.4, ss.1398-1407, 2021 (Hakemli Dergi)
With the increasing use of social media in recent years, there are too many comments to be followed on almost every issue. These comments contain both important and unimportant information. But, it is almost impossible to follow of so many comments nowadays. In this study, text classification of user comments made to the Anadolu University mobile application was made. It was estimated whether the comments made on the application were related to the content or the application. In addition, the effect of oversampling and undersampling on text classification performance was investigated. For this purpose, synthetic minority oversampling technique (Smote), condensed nearest neighbor undersampling technique (CNN) and random undersampling (RUS) technique were applied to the data set. 1008 user comments received from mobile application were classified by these techniques. In the Smote oversampling classification, ANN algorithm was found to have the best classification with 93.57% accuracy. In the undersampling classification, Random Forest algorithm was found to have the best classifications with 72.22% accuracy. In the random sampling classification, Extreme Gradient Boosting algorithm was found to have the best classification with 84.44% accuracy.