## 4 Experimental Results and Discussions  
In this study, we conducted comprehensive experimental analyses to evaluate the performance and effectiveness of the FSMTSA dataset in Turkish Sentiment Analysis tasks. These analyses leveraged a combination of advanced deep learning-based language models and traditional machine learning algorithms to ensure a robust comparison. The deep learning models utilized in the experiments include BERT Base Turkish, XLM-RoBERTa Base, TurkishBERTweet, Turkish RoBERTa Base, and ELECTRA, representing state-of-the-art approaches in natural language processing. Meanwhile, the traditional machine learning algorithms assessed in the study encompass Naive Bayes, Linear SVC, Logistic Regression, KNN, and Decision Tree, providing a comprehensive baseline for performance evaluation. The data were labeled as \(-1\) for negative sentiment polarity, 0 for neutral polarity, and 1 for positive sentiment polarity. In the studies conducted with language models, before training the model, the negative polarity data were encoded as 0, the neutral ones as 1, and the positive ones as 2. The dataset was divided into a training set of 12,682 samples, a validation set of 1585 samples, and a test set of 1586 samples before the training process. Afterward, the text was converted into an appropriate input format for the language model or algorithm being used. In the first part of the experiment, training, validation, and testing phases were applied sequentially with language models. Among the language models, Electra Base Turkish, BERT Base Turkish, Turkish BERT Tweet, and Turkish RoBERTa Base performed better since they are pre-trained models for Turkish text data. BERT Base Multilingual and XLM-RoBERTa Base models, on the other hand, are trained on more than 100 languages, including Turkish. Therefore, they also performed well for Turkish, which has fewer resources. The training parameters for the language models were set as follows: a learning rate of \(1\times 10^{-5}\), training and evaluation batch size of 36, weight decay of 0.1, 10 training epochs, and 300 warmup steps. These parameters were chosen to ensure fair and consistent experimental conditions across datasets. Specifically, we adopted the same hyperparameter settings as those used in the BounTi dataset experiments to maintain comparability and prevent any bias in performance evaluation. This approach allowed for a robust and objective assessment of our model's effectiveness under identical training conditions. To evaluate the performance of the models, standard classification metrics, including accuracy, precision, recall, and F1-score, were utilized. Accuracy measures the proportion of correctly classified instances across all predictions, providing an overall assessment of model correctness, and is computed using Equation (1). Precision, defined in Equation (2), quantifies how many of the instances predicted as positive are actually positive, ensuring that false positives are minimized. Recall, also referred to as sensitivity, evaluates the  
Figure 3: The histogram distribution of word lengths in the proposed FSMTSA dataset. model's ability to correctly identify actual positive instances, as shown in Equation (3), indicating how well it captures relevant cases. F1-score, which is the harmonic mean of Precision and Recall, is formulated in Equation (4) and offers a balanced measure by considering both false positives and false negatives, making it particularly useful when dealing with imbalanced data. These metrics were computed using standard formulas, where True Positives (TP) denote correctly classified positive instances, True Negatives (TN) represent correctly identified negative cases, False Positives (FP) occur when negative instances are misclassified as positive, and False Negatives (FN) refer to positive instances incorrectly predicted as negative. By incorporating these evaluation metrics, a comprehensive assessment of model performance was ensured.