Mamdani ve Sugeno Tip Bulanık Çıkarım Sistemleri ile Sosyal Medya Haber Popülerliğinin Tahmini

Year 2022, Volume: 14 Issue: 3, 303 - 320, 31.12.2022

İsmail Atacak Ömer Ayberk Şencan

https://doi.org/10.29137/umagd.1169623

Cited By: 1

Abstract

Haber popülerliği, internet ya da sosyal ağ sitelerinde yayınlanmış haberlerin ilgi düzeyinin ölçüsünün bir göstergesidir. Bu göstergenin değerinin bilinmesi, haber sağlayıcılarını rekabetçi ve kullanıcılar için okunabilirliği yüksek haberler yapmaya zorlar. Bu durum, hem haber servislerinin sürekliliğine hem de haber kalitesinin artırılmasına önemli katkılar sağlar. Bu yüzden, haber popülerliğini otomatik olarak tespit eden sistemlerin olması günümüzde bir ihtiyaç haline gelmiştir. Bu çalışmada, Kaliforniya Üniversitesi (KU)-Irvine Makine Öğrenmesi Deposu veri tabanından indirilen veriler bileştirilerek oluşturulmuş dengesiz veri seti ve bu veri setinden Sentetik Azınlık Örnekleme Tekniği (Synthetic Minority Oversampling Technique (SMOTE)) ile üretilen dengeli veri setine Mamdani ve Sugeno tip bulanık çıkarım sistemi temelli modeller uygulanarak haber popülerliği tahmini yapılmıştır. Haber popülerliği tahmininde çıkarım yöntemleri ve durulaştırma yöntemlerinin farklı biçimde yapılandırılmasından oluşan 6’ sı mamdani tip bulanık çıkarım sistemini ve 2’ si sugeno tip bulanık çıkarım sistemini içeren toplam 8 bulanık mantık temellli tahmin modeli kullanılmıştır. Karışıklık matrisi metrikleri ve R2 eğrileri ile performansları değerlendirilen tahmin modellerine ait deneysel sonuçlar; dengesiz ve dengeli veri setlerinin her ikisinde de tüm metrikler açısından en iyi performansı mak-min çıkarım yöntemi ve ağırlık merkezi durulaştırma yöntemini kullanan Mamdani tip bulanık çıkarım sisteminin sağladığını göstermiştir. Ayrıca yaptığımız çalışmada kullanılan modelleri literatürdeki çalışmalar ile karşılaştırdığımızda, ağırlıklı ortalama yöntemini kullanan Sugeno tip bulanık çıkarım sistemi dışındaki bulanık mantık temelli modellerin literatürdeki modellerin en iyileri kadar rekabetçi bir performans sergileyebildiği görülmüştür.

Keywords

Sosyal Ağlar, Mamdani Tip Bulanık Çıkarım Sistemi, Sugeno Tip Bulanık Çıkarım Sistemi, Makine Öğrenmesi, Haber Popülerliği

Prediction of Social Media News Popularity with Mamdani and Sugeno Type Fuzzy Inference Systems

Abstract

News popularity is an indicator that measures the relevance of news published on the internet or social networking sites. Knowing the value of this indicator forces news providers to make news that is competitive and readable for users. This contributes to both the continuity of news services and the improvement of news quality. Therefore, it has become a necessity nowadays to have systems that automatically detect news popularity. In this study, news popularity prediction is made by applying Mamdani and Sugeno type fuzzy inference-based models to the unbalanced data set created by combining data downloaded from the University of California (UC)-Irvine Machine Learning Repository database and the balanced data set produced from this dataset by Synthetic Minority Oversampling Technique (SMOTE). A total of 8 fuzzy logic-based prediction models, 6 of which include Mamdani type fuzzy inference system and 2 of which include sugeno type fuzzy inference system, consisting of different configuration of inference methods and defuzification methods, are used to predict news popularity. Experimental results of prediction models, whose performances are evaluated with confusion matrix metrics and R2 curves, show that the best performance in terms of all metrics in both unbalanced and balanced datasets is provided by the Mamdani type fuzzy inference system using the max-min inference method and the centroid defuzzification method. In addition, when we compare the models used in our study with the studies in the literature, it is seen that the fuzzy logic-based models except Sugeno type fuzzy inference system using wtaver method can perform as competitive as the best models in the literature.

Keywords

Social Networks, Mamdani Type Fuzzy Inference System, Sugeno Type Fuzzy Inference System, Machine Learning, News Popularity

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