## 4 Experimental Results and Discussions
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Test Results**} \\ \hline
**Model** & **Accuracy** & **Recall** & **Precision** & **F1** \\ \hline Electra Base Turkish & **90.64** & **90.66** & **90.63** & **90.64** \\ \hline Bert Base Turkish & 90.22 & 90.25 & 90.22 & 90.21 \\ \hline TurkishBERTweet & 88.10 & 88.09 & 88.14 & 88.08 \\ \hline Bert Base Multilang & 84.76 & 84.81 & 84.93 & 84.76 \\ \hline Turkish RoBERTa base & 86.33 & 86.30 & 86.31 & 86.29 \\ \hline XLM-RoBERTa Base & 78.13 & 78.18 & 78.27 & 78.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Testing results of the models compared after training with the FSMTSA dataset. Figure 4: Confusion matrices of the trained language models using our proposed FSMTSA dataset. Figure 5 presents the confusion matrices for each machine learning algorithm trained. Correct predictions are concentrated along the diagonal of the matrices, demonstrating that the predicted labels align closely with the true values. This alignment underscores the effectiveness of the proposed model in accurately capturing the patterns inherent in the FSMTSA dataset, which includes nuanced sentiment information specific to Turkish. The performance comparison across different models on the FSMTSA and BounTi datasets (Tables 8 and 9) highlights significant variations influenced by dataset characteristics. Notably, the Electra Base Turkish model outperforms the other models in terms of accuracy, precision, recall, and F1 score on both datasets. Specifically, Electra achieved the highest validation accuracy of 91.56% and test accuracy of 90.64% on the FSMTSA dataset (Table 8). This superior performance may be attributed to Electra's replaced token detection mechanism, which potentially aligns well with Turkish's agglutinative structure and flexible syntax, enabling the model to better capture linguistic nuances. These results indicate the model's exceptional ability to capture semantic nuances within the FSMTSA data. Similarly, the precision, recall, and F1 metrics are consistently above 90%, reinforcing its robust performance across all evaluation dimensions. Conversely, the model's performance on the BounTi dataset drops considerably, with a validation accuracy of 72.52% and a test accuracy of 73.40% (Table 9). While Electra maintains a slight lead over other models for this dataset, its lower precision (69.81%) and F1 score (70.0%) on the test set suggest that \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Test Results**} \\ \hline
**Model** & **Accuracy** & **Recall** & **Precision** & **F1** \\ \hline Linear SVC & 88.36 & 88.36 & 88.43 & 88.38 \\ \hline Logistic Regression & 87.44 & 87.44 & 87.61 & 87.46 \\ \hline Naive Bayes & 85.77 & 85.77 & 85.73 & 85.70 \\ \hline KNN & 81.05 & 81.46 & 81.05 & 81.11 \\ \hline Decision Tree & 76.03 & 76.03 & 76.08 & 76.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The testing results of the models were compared after training with the FSMTSA dataset. Figure 5: Confusion matrices for the results of the tested machine learning algorithms using our proposed FSMTSA dataset. it struggles with the dataset's more diverse or potentially imbalanced distribution. This outcome may stem from the BounTi dataset containing more complex or noisier linguistic patterns compared to the FSMTSA dataset. Furthermore, the FSMTSA dataset's diverse and challenging structure ensures that the performance observed is not merely a result of larger datasets but reflects the model's ability to adapt and generalize across varied linguistic contexts. This adaptability indicates that Electra's architecture is not over-reliant on dataset size but rather leverages its structural advantages to effectively process nuanced language patterns, highlighting its practical applicability.