# Machine Learning-Based Text Classification Comparison: Turkish Language Context
###### Abstract  
The growth in textual data associated with the increased usage of online services and the simplicity of having access to these data has resulted in a rise in the number of text classification research papers. Text classification has a significant influence on several domains such as news categorization, the detection of spam content, and sentiment analysis. The classification of Turkish text is the focus of this work since only a few studies have been conducted in this context. We utilize data obtained from customers' inquiries that come to an institution to evaluate the proposed techniques. Classes are assigned to such inquiries specified in the institution's internal procedures. The Support Vector Machine, Naive Bayes, Long Term-Short Memory, Random Forest, and Logistic Regression algorithms were used to classify the data. The performance of the various techniques was then analyzed after and before data preparation, and the results were compared. The Long Term-Short Memory technique demonstrated superior effectiveness in terms of accuracy, achieving an 84% accuracy rate, surpassing the best accuracy record of traditional techniques, which was 78% accuracy for the Support Vector Machine technique. The techniques performed better once the number of categories in the dataset was reduced. Moreover, the findings show that data preparation and coherence between the classes' number and the number of training sets are significant variables influencing the techniques' performance. The findings of this study and the text classification technique utilized may be applied to data in dialects other than Turkish. Turkish texts; machine learning; text preprocessing; algorithm effectiveness +
Footnote †: journal:  
Support Vector Machine (SVM), Naive Bayes (NB), Long Term-Short Memory (LTSM), Random Forest (RF), and Logistic Regression (LR) algorithms are some of the ML techniques [8], and these were deployed in this study. Section 3 will go through these algorithms in further depth. The ML workflow typically includes three steps: data preparation, selecting the appropriate ML algorithms and variables, and evaluating and assessing performance [9]. Many studies on the use of ML for text classification have recently been published, including survey papers (e.g., [2; 10; 11; 12]), comparative analyses using different ML algorithms (e.g., [13; 14]), papers focusing on specific languages (e.g., [4; 15; 16]), and papers applying certain ML algorithms for text classification (e.g., [17; 18]), text classification performance (e.g., [19]), and text classification frameworks (e.g., [6]). However, the literature on text classification in the Turkish context is limited [20; 21]. Accordingly, this work tries to meet this demand by focusing on several ML algorithms for identifying Turkish texts. It is intended that the inquiry text will be sent to the relevant class quickly using ML algorithms, allowing the staff to react to letters more quickly. Another concern to consider is which ML classification algorithm is best for this task. The classification method is challenging in this study as all of the inquiries are in the Turkish composable language [22; 23]. The paper contributes in the following ways. We utilize data from customer inquiries that come to an institution to evaluate the proposed techniques. Classes are assigned to such inquiries, which are specified in the institution's internal procedures. This study utilized the customers' inquiries, which involved many spelling errors. Accordingly, to prepare these data for analysis, we first normalized them. The terms in the corpus were then morphologically analyzed into their basic forms. Furthermore, the stop word list was generated by examining the most repeated word groupings and removing them. Also, the repeated classes were simplified using the k-means technique during the preparation phase, and the number of classes was reduced. Therefore, the dataset became more accurate and equitable. The SVM, NB, LTSM, RF, and LR methods were used for data classification.