## 4 Experimental Results and Discussions
These findings underscore that while the FSMTSA dataset appears more conducive to yielding high classification performance, potentially due to cleaner or more homogeneous samples, the BounTi dataset presents challenges that impact the generalizability of even high-performing language models. The comparison with BERTurk, XLM- RoBERTa, and Bert Multilanguage further reinforces these observations. BERTurk, for instance, performs similarly well on FSMTSA (validation accuracy: 91.16%, test accuracy: 90.22%) but shows a larger performance decline on BounTi (validation accuracy: 70.6%, test accuracy: 69.2%). XLM- RoBERTa and Bert Multilanguage exhibit even more pronounced performance degradation on BounTi, with the latter scoring the lowest (validation accuracy: 67.4%, test accuracy: 66.8%). The Electra Base Turkish model's relatively consistent performance across datasets suggests that it may generalize better across varied data distributions compared to the other evaluated models. However, the significant performance gap under \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Validation**} \\ \hline
**Dataset** & **Model** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline FSMTSA & Electra Base & 91.56 & 91.57 & 91.61 & 91.58 \\ \hline BounTi & Turkish & 72.52 & 68.70 & 69.80 & 69.20 \\ \hline FSMTSA & \multirow{2}{*}{BERTurk} & 91.16 & 91.18 & 91.17 & 91.18 \\ \cline{2-5}  & & 70.60 & 73.00 & 71.50 & 74.50 \\ \hline FSMTSA & \multirow{2}{*}{XLM-RoBERTa} & 85.96 & 85.93 & 86.17 & 85.99 \\ \cline{2-5}  & & 73.40 & 68.90 & 70.80 & 69.60 \\ \hline FSMTSA & \multirow{2}{*}{Bert} & 78.15 & 78.16 & 78.14 & 78.13 \\ \cline{2-5}  & & 67.40 & 63.10 & 65.10 & 63.60 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of models’ validation performance on FSMTSA dataset vs. BounTi dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Test**} \\ \hline
**Dataset** & **Model** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline FSMTSA & Electra Base & 90.64 & 90.66 & 90.63 & 90.64 \\ \hline BounTi & Turkish & 73.40 & 69.81 & 70.36 & 70.00 \\ \hline FSMTSA & \multirow{2}{*}{BERTurk} & 90.22 & 90.25 & 90.22 & 90.21 \\ \cline{2-5}  & & 69.20 & 72.90 & 70.10 & 72.30 \\ \hline FSMTSA & \multirow{2}{*}{XLM-RoBERTa} & 78.13 & 78.18 & 78.27 & 78.11 \\ \cline{2-5}  & & 71.30 & 67.70 & 70.20 & 68.50 \\ \hline FSMTSA & \multirow{2}{*}{Bert} & 84.76 & 84.81 & 84.93 & 84.76 \\ \cline{2-5}  & & 66.80 & 63.10 & 65.80 & 63.60 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of models’ test performance on FSMTSA dataset vs. BounTi dataset. scores the necessity of dataset-specific model fine-tuning and highlights the importance of considering dataset variability in model evaluation. This research presents a significant practical contribution to Turkish sentiment analysis by introducing FSMTSA, a high-quality, balanced dataset that enables better model training and evaluation. The dataset's diversity across multiple domains ensures that sentiment models trained on FSMTSA are applicable to a wide range of real-world scenarios, including customer review analysis, brand reputation monitoring, and public sentiment tracking. However, some limitations must be acknowledged. First, while FSMTSA is diverse, it does not yet cover all possible textual domains, such as legal or medical texts, which may require further expansion. Second, FSMTSA's annotation process, despite using multiple reviewers and language models for quality control, may still contain subjective biases, which could impact classification accuracy in edge cases (but it is still much better than automatically annotated datasets). Finally, this study primarily focuses on traditional deep learning models and does not include recent advancements in large-scale generative models (e.g., GPT-based architectures), which could be explored in future work.