## 5 Conclusions  
This study underscores the critical role of sentiment analysis in extracting actionable insights from text data, particularly for the Turkish language. While sentiment analysis has achieved remarkable progress for widely spoken languages like English, Turkish presents unique challenges due to its agglutinative morphology, flexible syntax, and the scarcity of large-scale labeled datasets. Addressing these obstacles is vital to developing accurate and  
Figure 11: SHAP analysis visualization for positive, negative, and neutral sentences.  
robust sentiment analysis tools for Turkish-speaking regions. To tackle these challenges, we introduced a balanced and extensive dataset for Turkish sentiment analysis, comprising 2333 positive, 2334 neutral, and 2333 negative sentences. To enhance diversity and maintain balance, an expanded dataset with 15,853 examples was created using data augmentation techniques such as back-translation and synonym replacement. These datasets provide a valuable foundation for training and evaluating sentiment analysis models in Turkish.  
Using the proposed datasets, we trained and evaluated a comprehensive range of machine learning and state-of-the-art deep learning models. These included advanced architectures such as Electra Base Turkish, BERT Base Turkish, TurkishBERTIweet, BERT Base Multilanguage, Turkish RoBERTa Base, and XLM-RoBERTa Base, alongside traditional methods like Linear SVC, Logistic Regression, Naive Bayes, KNN, and Decision Tree. This rigorous evaluation offered insights into the relative performance of various models, highlighting their potential for Turkish sentiment analysis.  
The improvements in our dataset and model innovations have significant practical implications in various real-world applications. For instance, in customer service automation, our sentiment analysis model can enhance chatbot interactions by accurately understanding customer emotions, leading to more effective and empathetic responses. Similarly, in social media monitoring, the refined sentiment analysis capabilities can help businesses and policymakers track public sentiment more accurately, enabling better decision-making and crisis management. Furthermore, e-commerce platforms can leverage our model to analyze product reviews more effectively, improving recommendation systems and customer experience. These examples highlight the practical benefits of our study in addressing real-world challenges where sentiment analysis in the Turkish language plays a crucial role.  
Conceptualization, K.B.Z. and S.S.; methodology, S.S. and K.B.Z.; software, K.B.Z. and B.S.K.; validation, K.B.Z., S.Z.D. and B.S.K.; formal analysis, K.B.Z.; resources, K.B.Z., S.Z.D. and B.S.K.; data curation, K.B.Z., S.Z.D. and B.S.K.; writing--original draft preparation, K.B.Z., S.Z.D. and B.S.K.; writing--review and editing, S.S.; visualization, K.B.Z., S.Z.D. and B.S.K.; supervision, S.S.; project administration, S.S.; funding acquisition, S.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original datasets presented and used in the study are openly available in the FSMTSA GitHub repository at ([[https://github.com/keverbusrayildirim/FSMTSAD](https://github.com/keverbusrayildirim/FSMTSAD)]([https://github.com/keverbusrayildirim/FSMTSAD](https://github.com/keverbusrayildirim/FSMTSAD)), accessed on 15 January 2025).  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
NLPNatural Language Processing  
FSMTSADFatih [PERSON] Turkish Sentiment Analysis Dataset  
ElectraEfficiently Learning an Encoder that Classifies Token Replacements Accurately  
BERTBidirectional Encoder Representations  
RoBERTaA Kobusly Optimized BERT Pre-training Approach  
SVCSupport Vector Classifier  
KNNK-Nearest Neighbors  
XGBoosteXtreme Gradient Boosting  
MLPMulti-Layer Perceptron