# LSRM: A New Method for Turkish Text Classification
## 3 Materials and Methods
### Algorithms Used in the Study  
ML involves analyzing and interpreting complex datasets with various algorithms by constructing a model. Based on its experiences with past training data, researchers anticipate that the model will predict future data. One of the primary objectives of the developed classification algorithms in this context is to accurately classify the maximum volume of data using a minimum dataset. Analysis of the comparison-based studies in the literature revealed the use of traditional ML algorithms prevalently [32; 33]. As a result, this study also preferred using traditional ML algorithms such as NB, SMO, KNN, J48, and RF, typically the most often cited in the literature. NB: Based on [PERSON] \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author** & **ML** &
\begin{tabular}{c} **Word** \\ **Embedding** \\ \end{tabular} & **DL** & **TL** \\ \hline [PERSON] [27] & Yes & Yes & No & No \\ [PERSON] et al. [28] & Yes & Yes & No & No \\ [PERSON] et al. [29] & No & Yes & Yes & Yes \\ [PERSON] [13] & Yes & Yes & Yes & No \\ [PERSON] [30] & Yes & Yes & Yes & No \\ [PERSON] [31] & Yes & No & No & No \\ The present study & Yes & Yes & Yes & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of works regarding the Turkish TC. theorem, it is also possible to use this algorithm effectively in small datasets. NB is frequently used in binary and multiple classification tasks due to its high performance, speed, and resistance to noise [34]. SMO: As frequently used in optimization problems, SMO extrapolates the result by solving even the minimal optimization problem. It attempts to solve complex problems by optimizing two variables at each step and keeping the other variables constant. This algorithm, used to train support vector machines, substitutes the missing data and converts categorical features into a binary form [35]. KNN: It is a learning algorithm that performs classification using all training data. It assesses the similarity status of the new data using the information in the training dataset and returns the most suitable classification. It is robust against noisy training data but has a low efficiency in big datasets [36]. J48: This ML algorithm, which uses the decision tree, calculates entropy and aims to maximize the gain while generating the decision tree. Recursive classification of the data provides high accuracy, whereas it may result in excessive conventions [37]. RF: It is a collective classification algorithm developed by [PERSON] and [PERSON] and gets updates with new versions regularly. In the RF algorithm, decision trees that create the forest from different examples are combined using the Bootstrap method. This procedure works with the original dataset. Multiple decision tree structures are used in the training part of the dataset [33]. DL algorithms, which process data in big datasets and generate outputs from patterns in the data, are a method developed to solve complex problems using artificial neural networks. It is currently used in diverse fields, including biomedical signal processing, text processing, and image processing. The most well-known and widely used algorithms in these fields are CNN, LSTM, and GRU [38]. CNN: CNN, a DL algorithm with multiple layers of artificial neural networks, is a forward-looking neural network used in various domains such as text categorization, audio processing, and image processing. The core of the CNN algorithm is a convolutional function, which uses particular filter sizes on an input dataset and performs a matrix multiplication operation. The convolution operation plays a critical role in learning the dataset features [39]. LSTM: It is a DL architecture performed for processing sequential data, such as data with time series. LSTM consists of interconnected nodes to model long-term dependencies, and each node retains a hidden cell and three gates, namely the input gate, output gate, and forget gate. These gates control transitions by determining the amount the generated data will pass through.