# LSRM: A New Method for Turkish Text Classification
## 3 Materials and Methods
### Algorithms Used in the Study
LSTM predicts the next value or classifies data using previous data from its current state. Therefore, LSTM is often used to classify and analyze data in time series [40]. GRU: It is a DL algorithm used in diverse domains requiring data integrity, especially NLP, translation, and speech recognition. In the GRU algorithm, memory cells carry out the learning process using former and newer data. GRU stores former data in memory cells by connecting with different network layers. Subsequently, these memories are used in future data processing. As a result, the GRU algorithm is particularly effective for processing long texts or speech and is ideal for processing sequential data, such as NLP [41]. The TL model, an artificial learning algorithm, is frequently used in NLP and TC tasks. Unlike other language models, The TL model uses a DL architecture based on parallel processing. Thus, it allows us to train the model faster and generate accurate results [42]. The literature has a variety of TL algorithms, such as BERT and RoBERTa. BERT: Google researchers trained it in 2018 on two model dimensions: BERTBASE and BERTLARGE. The BERT language model has a bidirectional transformer architecture; hence, it uses a bidirectional neural network while pre-training the model with unsupervised learning. Fine-tuning operations are necessary to train this model specifically for the task [42]. BERT also has a BERTurk model specifically designed for the Turkish language. It is a pre-trained model to process Turkish texts based on the features specific to the Turkish language and is a language model trained by BERT. Therefore, it may be an effective alternative for Turkish TC problems. BERTurk is built to perform word learning, word semantics, sentiment analysis, TC, and other NLP tasks [43; 44]. RoBERTa: As designed by developing the BERT model, RoBERTa was trained using a larger volume of data. It is also a DL model developed by Facebook in 2019 and used in NLP. Based on the BERT model, RoBERTa was designed to minimize the adverse effects of learning efficiency and external noise on the model performance, which are considered poor in the BERT model [45]. Neural language models can achieve high classification performance despite less manual preprocessing of semantic and syntactic texts. One of the most significant issues in TM is representing training data as a vector. Each feature vector represents a word in the training data. The vector representation method used here offers promising results in natural language tasks. The names of the four most widely used WEb systems in the literature are Word2 vec, Doc2 Vec, FastText, and Glover [46; 47; 48]. Word2 Vec is a pre-trained technique used to represent the contextual meaning of a word in a text. As a prediction-based model that attempts to identify words in a vector space, Word2 Vec works based on giving meaning to a word and using other words surrounding that word. It works with the CBOW (Continuous Bag of Words) and Skip-Gram sub-methods. Word2 Vec mostly requires a large training dataset. As one of the significant hyperparameters for Word2 Vec, an artificial neural network consisting of input, output, and hidden layers, the window_size parameter indicates how many words can be on both sides of the target word. Word2 Vec represents each word as a vector. The position of each word in this vector is related to the contextual existence of that word with others. While the CBOW architecture predicts existing words based on context in the model, the Skip-Gram architecture, on the other hand, predicts the words around the already given word. It is possible to use Word2 Vec for many applications, including TC, NLP, and word similarity identification [46]. In essence, the Doc2 Vec model resides on the principles of the Word2 Vec model and generates vectors at the document level instead of words. This model works similarly to the Word2 Vec architecture, whereas it uses the method of adding a separate document vector for each document. Doc2 Vec is a considerably popular algorithm.