## 1 Introduction
The amount of digital data resources related to Turkish is quite limited. This highlights the need for more comprehensive efforts to expand the scope and diversity of datasets prepared in the Turkish language. Enriching Turkish digital data resources will lead the development of language-focused datasets and support advancements in the field. The primary objective of this study is to provide resources for the development of prediction models specific to the Turkish language and to establish a new basis for evaluating the performance of existing models. This approach seeks to gain a deeper understanding of both the dataset's and the models' ability to capture and analyze the complex linguistic structure of Turkish. Additionally, finding neutral data in Turkish datasets presents a significant challenge, as many existing datasets often include non-sensical or irrelevant information labeled as neutral. To address this deficiency, the dataset we prepared includes many neutral labels sourced from categories such as tweets and product reviews. This ensures that the models are trained to distinguish neutral and diverse sentiments effectively, supported by high-quality and reliable data. The proposed dataset contains 2333 positive, 2334 neutral, and 2333 negative sentences, ensuring a balanced structure that facilitates fair and comprehensive model training. Balanced datasets are critical for achieving accurate and consistent results across different sentiment classes. Two data augmentation techniques, back-translation and synonym replacement were applied to enhance model performance and increase data diversity. Back-translation involves translating a sentence into another language and then back into the original language to introduce linguistic variations, while synonym replacement substitutes words with their synonyms to create alternative sentence structures while preserving meaning. To maintain the dataset's balance, a second version expanded dataset was created, comprising 15,853 examples: 5284 positive, 5206 neutral, and 5363 negative sentences. Using the proposed dataset, a range of machine learning and deep learning models were trained and evaluated. This study employed state-of-the-art deep learning architectures specifically designed or fine-tuned for the Turkish language, including ELECTRA Base Turkish (Efficiently Learning an Encoder that classifies Token Replacements Accurately) [7], a pre-trained transformer model optimized for token-level replacement tasks, which improves training efficiency by focusing on detecting replaced tokens. BERT Base Turkish (Bidirectional Encoder Representations from Transformers) [8], a bidirectional transformer model pre-trained on Turkish text, is particularly effective in capturing contextual relationships through its transformer-based attention mechanisms. Additionally, TurkishBERTweet [9], tailored for Turkish social media data, effectively handles informal and colloquial text, and BERT Base Multilanguage [10], capable of processing over 100 languages including Turkish, provides robust cross-lingual capabilities. Turkish RoBERTa Base (A Robustly Optimized BERT Approach) [11] offers improved performance by fine-tuning on Turkish-specific datasets, while XLM-RoBERTa Base (Cross-lingual Language Model Robustly Optimized BERT Approach) [12] is a versatile model pre-trained on multiple languages, making it suitable for cross-lingual tasks. Alongside these advanced models, traditional machine learning methods were also explored. For instance, the Linear Support Vector Classifier (SVC) [13] is highly effective in handling high-dimensional data by finding the optimal hyperplane for classification, while Logistic Regression [14] provides a probabilistic approach for binary or multi-class classification tasks. Naive Bayes [15], known for its simplicity and efficiency, performs well in text classification by utilizing probabilistic methods. K-Nearest Neighbors (KNN) [16] relies on distance-based similarity measures to classify data points, making it intuitive for small datasets.