# LSRM: A New Method for Turkish Text Classification
## 1 Introduction  
The advancements in digital technology, the escalating internet speeds, and the increasing volume of unstructured data generated by social networking have reached incredible dimensions [1]. Data mining techniques, machine learning (ML) algorithms, artificial intelligence, and statistical analysis techniques are used to search for and identify the desired data from these vast volumes of unstructured data documents. It is also possible to perform knowledge discovery (KD) through text mining (TM), a sub-domain of the data mining process. The text classification (TC) method is one of the most typically used approaches in TM studies. The TC method involves creating a model using a predefined dataset and aims to assign uncategorized data to a correct category [2]. Accordingly, TC refers to a process that assigns a text to a specific class based on its content. Class assignment processes may also involve categorizing diverse text types, including TC, e-mail classification, news filtering, and document editing, which explicitly require datasets and ML algorithms [3, 4]. In addition to basic ML algorithms such as Naive Bayes (NB), Sequential Minimal Optimization (SMO), K-Nearest Neighbors (KNN), Random Forest (RF), and J48, several deep learning (DL) algorithms, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), have been used for TC. These algorithms have even functioned in Word2 Vec, Doc2 Vec, Glove, and FastTest-type Word Embedding (WEM) systems with the idea that DL may improve their performance [5, 6]. Additionally, researchers have developed various algorithms for TC, such as Transformers Learning (TL) algorithms Bidirectional Encoder Representation from Transformers (BERT) and Robustly Optimized BERT Pre-Training Approach (RoBERTa), and used them in numerous text analysis studies [5, 6, 7, 8]. Besides these algorithms, feature engineering approaches in text documents, including traditional surface classification models, n-gram models, term weighting schemes, part-of-speech (POS) labels, latent Dirichlet allocation, and other lexical features, have also been used for TC [6]. The review of the subject-related literature revealed that fundamental studies widely utilized traditional approaches forTC. These studies primarily used ML algorithms, such as decision trees, NB, and support vector machines [5; 6; 7; 8]. The tests performed in these studies mostly failed to determine the relationship between the context and the word; hence, they observably generated limited success rates. However, the use of different DL algorithm datasets in recent studies has indicated that DL algorithms play a crucial role in increasing classification performance [3]. Finnish and Hungarian-like languages, which resemble Turkish in terms of language structure, belong to the class of agglutinating languages. Words in these languages acquire diverse grammatical functions with suffixes added to their roots. Features such as person, time, direction, and situation are expressed by adding suffixes to the word roots, and words are formed with each suffix holding a specific meaning. In these languages, morphological analysis is rather detailed, and combining roots with various suffixes results in a vast array of new word forms. With the agglutination method, in other words, the process of merging roots and suffixes to each other sequentially, each suffix specifies the grammatical structure of the word by adding a new component to the meaning of the word. Various academic studies in the literature have strived to find the meaning of the word roots in these languages, which are in the Ural-Altaic language family like Turkish. As a result, these studies listed roots and root-related suffixes separately and analyzed the formation of the word roots [9; 10; 11; 12]. It is challenging to separate compound terms in Turkish, since it is an agglutinative language.