## 2 Related Work  
This section examines studies in the field of sentiment analysis in the current literature and the techniques employed in these studies. In recent years, numerous studies have focused on automatically detecting emotional tendencies in texts. These studies primarily aim to detect emotional tendencies in various data sources such as social media, customer feedback, and news content. Several studies in the literature have explored different approaches for sentiment analysis, including traditional machine learning methods such as Support Vector Machines (SVMs) [18] and Naive Bayes, deep learning models like Long Short-Term Memory (LSTM) [19] and Convolutional Neural Network (CNN) [20], and hybrid architectures that combine rule-based [21] and learning-based approaches [22]. In machine learning methods, text analysis is performed using word-based statistical models. Deep learning methods typically use powerful models that can capture more complex language patterns and contextual relationships. Hybrid models usually involve combining different models for classification purposes [23; 24]. Regarding machine learning approaches, authors used classification algorithms such as Naive Bayes, Logistic Regression, SVM, KNN, Decision Tree, Extreme Gradient Boosting (XGBoost) [25], Adaptive Boosting (AdaBoost) [26], and Random Forest [27]. For deep learning and transformer models, authors used Multi-Layer Perceptron (MLP) [28], LSTM, CNN, Recurrent Neural Network (RNN) [29], BiLSTM (Bidirectional Long Short-Term Memory) [30], BERT, RoBERTa, Generative Pre-trained Transformer 3 (GPT-3) [31], Large Language Model Meta AI (Llama) [32], and hybrid models. Hybrid approaches, which combine multiple techniques for improved performance, have also gained attention in recent studies. For example, CNN-BiLSTM models integrate convolutional layers for feature extraction and BiLSTM layers for capturing long-range dependencies, enhancing sentiment classification accuracy [33]. Additionally, lexicon-based and machine learning hybrid approaches leverage rule-based sentiment scoring combined with supervised learning algorithms to improve robustness [34]. Ensemble-based hybrid models, such as those incorporating Random Forest and Gradient Boosting classifiers, have also been proposed to enhance sentiment classification reliability [35]. These approaches require two datasets for training and evaluation. The training dataset is used for the algorithm to learn the features of the relevant domain, while the evaluation dataset is used to validate the model created from the training dataset. Hyperparameter tuning in models impacts the performance of the classifier, so fine-tuning is observed in most models. Figure 1: Sentiment analysis process flow. The success of a model depends on the effectiveness of the method used in the feature extraction phase. Among the most frequently used feature extraction methods are Bag of Words, Term Frequency-Inverse Document Frequency (TF-IDF), n-grams (unigrams, bigrams, and trigrams), POS tagging-based features, and dependency rule-based features [36; 37; 38; 39; 40; 41; 42; 43; 44; 45]. In [24], a hybrid sentiment analysis model for Turkish texts was presented by combining lexicon-based methods and machine learning classifiers. Lexicon-based methods rely on predefined word lists containing words and synonyms that represent emotional states, followed by the calculation of a predicted sentiment score [36]. In this approach, the polarities of words in tweets were evaluated using the open-source Orange program, and positive, negative, or neutral labels were assigned [24]. [PERSON], [PERSON] et al. [37] proposed a TF-IDF-based approach for improving the perception of malicious actions' emotions and contexts by offering a more robust, adaptable, and sustainable method. This study also employed various modern optimization methods, including a modified version of the Red Fox optimization algorithm, for hyperparameter tuning.