Türkçede Yüksek Doğruluklu Duygu Tespiti için Makine Öğrenmesi Sınıflandırıcılarının Optimizasyonu

Abstract

Duygu analizi, metinsel verilerden görüşleri ortaya çıkarmak için yaygın olarak kullanılmaktadır; ancak Türkçe gibi morfolojik açıdan zengin dillerdeki uygulamaları zorluğunu korumaktadır. Bu çalışma, birleşik bir deneysel çerçeve altında, ikili (binary) Türkçe duygu analizi için klasik makine öğrenmesi sınıflandırıcılarının ve topluluk öğrenme (ensemble learning) stratejilerinin optimizasyonunu incelemektedir. Dengeli bir kullanıcı yorumları veri seti üzerinde, doğrusal destek vektör makinesi sınıflandırıcısı (LSVMC), çok terimli Naïve Bayes (MNB) ve lojistik regresyon (LR) dahil olmak üzere çeşitli makine öğrenmesi modelleri eğitilmiştir. Bu modellerin çıktıları, çoğunluk oylaması (MVEL) ve yığınlama (SEL) gibi topluluk öğrenme yöntemleri kullanılarak birleştirilmiştir. Sonuçlar, SEL sınıflandırıcısının artan hesaplama karmaşıklığı pahasına %92,80 doğruluk oranına ulaşarak incelenen tüm modellerden daha iyi performans gösterdiğini ortaya koymaktadır. İncelenen bireysel modeller arasında en yüksek doğruluk oranlarını LSVMC (%92), MNB (%92) ve LR (%92) sergilemiştir. Çalışma, derin öğrenme veya transformatör tabanlı mimariler aracılığıyla literatürdeki en ileri düzey (state-of-theart) performansı geride bırakmayı hedeflemese de, sonuçlar, optimize edilmiş klasik modellerin Türkçe duygu analizinde halen oldukça başarılı olduğunu ve performanslarının topluluk öğrenme mekanizmalarıyla daha da geliştirilebileceğini kanıtlamaktadır

Keywords

• Makine Öğrenmesi
• Topluluk Öğrenmesi
• Türkçe Dil İşleme
• Türkçe Duygu Analizi

Supporting Institution

Hasan Kalyoncu Üniversitesi / Türkiye

Ethical Statement

Bu araştırma, yazarlar tarafından insan katılımcılar veya hayvanlar üzerinde gerçekleştirilen herhangi bir çalışmayı içermemektedir. Bu çalışmada analiz edilen veri seti, Hacettepe Üniversitesi/Türkiye resmi web sitesinden akademik araştırma izni alınarak elde edilmiş olup, bu nedenle ek bir etik onayına ihtiyaç duyulmamıştır.

Thanks

Bilimsel araştırma derslerinde bana öğrettiği araştırma becerileri için Dr. İbrahim Halil Değer'e teşekkür etmek istiyorum. Ayrıca, bu araştırmayı tamamlamam için gerekli bilgi ve uzmanlığı bana sağlayan herkese teşekkür etmek istiyorum.

OPTIMIZING MACHINE LEARNING CLASSIFIERS FOR HIGH-ACCURACY SENTIMENT DETECTION IN THE TURKISH LANGUAGE

Abstract

Sentiment analysis is widely used to extract opinions from textual data; however, its application to morphologically rich languages such as Turkish remains challenging. This study investigates the optimization of classical machine learning classifiers and ensemble learning strategies for binary Turkish sentiment analysis under a unified experimental framework. Several ML models are trained on a balanced dataset of user reviews, including Linear Support Vector Machine Classifier (LSVMC), Multinomial Naïve Bayes (MNB), and Logistic Regression (LR). Their outputs were further combined using Ensemble Learning (EL) models, namely Majority Voting (MVEL) and Stacking (SEL). Results demonstrate that the SEL Classifier outperforms all examined models, achieving 92.80% accuracy at the cost of increased computational complexity. Among the examined individual models, LSVMC (92%), MNB (92%), and LR (92%) had the best accuracy. While the study does not aim to achieve state-of-the-art performance with deep or transformer-based architectures, the results demonstrate that optimized classical models remain highly effective in Turkish SA, and their accuracy can be further improved with EL mechanisms.

Keywords

• Machine Learning
• Ensemble Learning
• Turkish Language Processing
• Turkish Sentiment Analysis

Supporting Institution

Hasan Kalyoncu University / Turkey

Ethical Statement

This research did not involve any studies with human participants or animals performed by the authors. The dataset analyzed in this study was obtained with permission for academic research from Hacettepe University/Türkiye official website, and therefore no further ethical approval was required.

Thanks

I would like to thank Dr. İbrahim Halil Değer for the research skills he taught me in the scientific research classes. I would also like to thank everyone who provided me with the necessary knowledge and expertise to complete this research.

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