# Comparison of Machine Learning Models for Sentiment Analysis of Big Turkish Web-Based Data +
## 3 Materials and Methods
### System Evaluation  
At this study stage, the success of manually labeled data performed with sentiment analysis was tested with various machine learning algorithms. The success of the proposed model in the positive, neutral, and negative classifications was evaluated with K-NN, SVM, DT, RF, and LR. Sentiment analysis is a real-time computational technique [26] that tries to understand and explain sentiments by analyzing large amounts of text data in a way that helps people make decisions [27]. As part of AI technology, sentiment analysis is a meaningful analysis method that obtains text sentiment trends and polarity [28]. It is located at the intersection of natural language processing and large-scale data mining [29]. It identifies, classifies, and evaluates human sentiments' polarity in subjective documents or texts. One of the most widely used strategies in sentiment analysis is machine learning. Machine learning is an approach that largely overlaps with statistics, and most evaluationsare empirical [5]. With machine learning, a model is created with data using a good and helpful approach and is used to solve real-world problems [6]. Machine learning algorithms learn the characteristics of categories from a set of classified text and use them to sort documents into predefined categories after building an automatic classifier [30]. It is a part of artificial intelligence that provides the benefit of automatically extracting information from texts and concepts without direct and explicit programming [31]. Thus, the machine learning approach is a branch of artificial intelligence that develops techniques that allow computers to learn and aims to create generalizable programs from unstructured data [32]. K-NN is a non-parametric method used to perform robust classification or regression, especially for sentiment analysis [33], and is applied to estimate the probabilities of unseen co-occurrences [34]. The classification method selects K-nearest neighbors in training documents and classifies an unannotated document according to these K-neighbors [35]. In the training phase, only the feature vectors and categories of the training set are stored [30]. It performs classification by assigning unlabeled observations to the class of the most similar labeled samples [36]. In the algorithm, the 'K' value indicates the number of neighbors used in the model's estimation; however, in this study, K = 5 was used by default. Thus, class estimation was performed by looking at the 5 closest neighbors of the model. SVM is used to perform subjective and objective classification and polarity classification applications [37]. The algorithm aims to select a hyperplane that maximizes the margin between the closest examples of two different classes [38] or can perform a classification that can discriminate between a certain amount of data from the training set [39]. It tries to find the best separation and classification between the data using support vectors [40]. The most appropriate hyperplane to be selected with the algorithm separates the data into two groups [41]. It finds a hyperplane separating two labeled classes with a maximum margin, which is shown as the distance between the two hyperplanes [42]. In this study, kernel = rbf (radial basis function) was used by default and a hyperparameter that determines the tolerance to error was used with the value C = 1.0. Decision tree learning is one of the predictive modeling methods related to machine learning and is frequently used in data mining [43]. The essence of a decision tree is to learn from supervised data with association logic [44]. It generally represents and visualizes a decomposition of the combination of constraints of instances on feature values [45]. As a decision-making technology, the decision tree uses a tree diagram to represent the expected value of each decision [46]. The decision tree is a tree structure for manually categorizing training documents by generating true/false queries.