## 3 Dataset
Through these augmentation methods, the total number of data points was increased to 15,853, comprising 5284 positive, 5284 neutral, and 5363 negative examples, as detailed in Table 3. During the augmentation process, potential duplicates were systematically identified and removed to ensure the dataset's quality and uniqueness. While the original dataset maintains a perfectly balanced structure, the slight imbalance in the expanded dataset arises from the nature of data augmentation methods. Synonym replacement provides more variations for positive and negative words, while back-translation may introduce subtle shifts in neutrality. To preserve data quality, linguistic integrity was prioritized over exact numerical balance. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Text** & **Type** & **Sentiment** \\ \hline Aksam 9 da kapanma olacak ya sannrm & & \\ lstanbul’un trafik yogunlußunun \%50 si suan & Tweet & \(-1\) \\ Yeniköy \(\cdot\)de bu ne hal? [[https://t.co/ot](https://t.co/ot)]([https://t.co/ot](https://t.co/ot)) & & \\ \hline Vatandaslar, oy kullanma hakkina sahiptirler, & & LLM-generated & 0 \\ ulaslabilirlik konusuna dikkat edilmektledir. & & \\ \hline lyi daha guzel yapabilir & Product comments & 0 \\ \hline Kokusu guzel hahif diger yumusatclar gibi & & \\ agir yogun bir kokusu yok. Bahar gibi & & \\ kokuyor bahar aylarinda terchi edilebilecek & & \\ bir yumusatcln bence. Fiyat cok uygundu & & \\ indirimdeyken aldm. Hafif bahar kokulari & & \\ sevenlere tavsiye ederm. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Some samples from the proposed the FSMTSA dataset. \begin{table}
\begin{tabular}{c c} \hline \hline
**Sentiment** & **Total** \\ \hline Positive & 5284 \\ \hline Neutral & 5206 \\ \hline Negative & 5363 \\ \hline Total & 15,853 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Distribution of the augmented FSMTSA dataset. As a result of these enhancements, the proportions of positive, negative, and neutral classes in the expanded dataset were adjusted to 33.3%, 33.8%, and 32.8%, respectively. The dataset annotation process was conducted manually. Each data sample was labeled by three different annotators. The majority votes for each data sample were reviewed by a supervisor. In cases of disagreement among the annotators, the responses were compared with the outputs of at least three different Large Language Models (LLMs), and the annotation for that sample was reperformed from scratch. This ensured that the data were accurately assigned to the correct class. The sentiment classes of the data were encoded as \(-1\), \(0\), and \(1\), representing negative, neutral, and positive sentiments, respectively, as shown in Table 4.  
Data preprocessing for this study involved several crucial steps to ensure data quality and suitability for analysis. For example, Twitter data were collected using the Twitter API by filtering based on relevant keywords and hashtags. Subsequently, both Twitter and review website data underwent a cleaning process, including removing irrelevant characters, HTML tags, URLs, and excessive whitespace. Special attention was paid to handling emojis and emoticons; these were either preserved for sentiment analysis or converted to their textual representations, depending on the specific task. Duplicate entries were identified and removed to avoid bias and inflate statistical significance. Text normalization techniques were employed for sentiment analysis, including lowercasing all text and handling contractions. Stop words, common words with little semantic value, were removed to focus on more informative terms. Finally, the text data were tokenized, splitting it into individual words or phrases for subsequent analysis. This comprehensive preprocessing pipeline aimed to minimize noise, standardize the data format, and enhance the signal-to-noise ratio for improved model performance. The data were not pre-split into training, validation, and test sets; this division was made prior to model training.