## 3 Dataset  
In sentiment analysis research, open-source datasets containing positive, negative, and neutral categories are significantly limited compared to those excluding the neutral class. Furthermore, sentiment categories (positive, negative, and neutral) can be classified as categorical or fine-grained polarities. In studies utilizing fine-grained sentiment polarity, data points slightly leaning toward positive or negative from neutral can be more precisely adjusted, facilitating classification. Conversely, in categorical sentiment polarity, the definitive classification of neutral sentiment poses greater challenges. These difficulties often lead to issues such as the mislabeling of neutral data during dataset preparation, which subsequently reduces the accuracy of models trained on these datasets. Consequently, many sentiment analysis studies rely on datasets that do not include a neutral category. Additionally, most existing open-source datasets are designed for widely spoken languages such as English, underscoring the need for a Turkish sentiment analysis dataset that includes neutral sentiment. To address this gap, the Fatih Sultan Mehmet Turkish Sentiment Analysis Dataset (FSMTSAD) was developed, offering a balanced and comprehensive resource for Turkish sentiment analysis studies. A review of the existing literature and datasets revealed a dataset similar to this study: the BounTi [62] dataset, developed by [PERSON] et al. The BounTi dataset comprises Turkish tweets about universities in Turkey, manually labeled as positive, neutral, or negative. As presented in Table 1, this dataset includes 2348 positive, 4215 neutral, and 1401 negative entries, amounting to a total of 7964 data points. Existing datasets often suffer from an imbalance in sentiment class distribution, with significantly fewer neutral examples, which exacerbates the difficulty of distinguishing the neutral class from positive and negative sentiments. This lack of balance not only hampers the ability to identify neutral sentiments but also impacts the overall robustness of models trained on such datasets. To address both the scarcity of neutral data and the broader class imbalance, FSMTSAD was meticulously constructed to achieve an equal representation of all sentiment classes. As shown in Table 2, FSMTSAD contains 2333 positive, 2334 neutral, and 2333 negative samples, ensuring that each class constitutes approximately 33.3% of the dataset. As shown in Table 3, the augmented version of FSMTSAD ensures that FSMTSAD maintains its balanced structure while providing a greater representation of neutral data compared to existing benchmarks. This balanced and augmented distribution enables models to effectively distinguish neutral sentiments while also mitigating the biases introduced by disproportionate class representations in other datasets. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sentiment** & **Training** & **Validation** & **Test** & **Total** \\ \hline Positive & 1691 & 188 & 469 & 2348 \\ \hline Neutral & 3034 & 338 & 843 & 4215 \\ \hline Negative & 1008 & 113 & 280 & 1401 \\ \hline Total & 5733 & 639 & 1592 & 7964 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Distribution of data in the BounTi dataset. \begin{table}
\begin{tabular}{c c} \hline \hline
**Sentiment** & **Total** \\ \hline Positive & 2333 \\ \hline Neutral & 2334 \\ \hline Negative & 2333 \\ \hline Total & 7000 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Distribution of data in the FSMTSA dataset. In this study, two different data augmentation methods were employed to improve model performance and enhance data diversity. The first method involved the application of the back-translation technique, in which the original texts were translated into another language and then back into the original language, as illustrated in Figure 2. This process generated various textual variations.