Аңдатпа
Sentiment analysis for the Turkish language is particularly challenging due to the language's agglutinative nature, its complex morphology, and the relative paucity of high-quality labeled datasets compared to English. This paper presents a comprehensive comparative study of different feature representation methods and classification algorithms for Turkish sentiment analysis. We benchmark a traditional machine learning baseline-TF-IDF combined with Random Forest-against several Long Short-Term Memory (LSTM) deep learning models. To address lexical sparsity, we examine four embedding strategies: randomly initialized embedding layers, custom Word2Vec embeddings trained on our corpus, and pre-trained Turkish FastText models in both trainable and non-trainable configurations. Our experiments, conducted on three publicly available datasets, demonstrate that LSTM models consistently outperform the statistical baseline, providing a significant accuracy boost over TF-IDF. Notably, the non-trainable pre-trained Turkish FastText embeddings achieved the best performance, reaching a peak accuracy of 95.33%. These results indicate that leveraging large-scale external resources is particularly effective for morphologically rich languages, compared to relying solely on task-specific data. Moreover, freezing the weights of a pre-trained model can help prevent overfitting, yielding more robust generalization. This study provides a clear roadmap for researchers and practitioners in selecting the most effective embedding models for Turkish natural language processing tasks.