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