# Machine Learning-Based Text Classification Comparison: Turkish Language Context
## 2 Literature Review
### Text Classification in Turkish Context  
[PERSON] and [PERSON] [24] investigated the SVM, NB, and RF algorithms in terms of the document type, name of the author, and author's race. For text classification, they employed the character N-grams approach. The writings of 18 authors, 4 females and 14 males, with 35 distinct texts authored by every author, and common interests and athletics, were analyzed. The correlation-based feature selection approach was utilized,which was found to improve classification accuracy. The best functioning method in author discovery was found to be the NB method, and the best working algorithm in race and genre identification was found to be the SVM [24]. [PERSON] et al. [25] used DT-J48, K-Nearest Neighbor (K-NN), and Bayesian Probabilistic classifiers to classify text using the N-gram method. They looked into 600 text documents that were obtained online. These documents were represented by the researchers using TF-IDF. They also used stemmed words on the data to minimize the vector space. They changed the text to lowercase in data preparation, then applied data cleansing to create and stop the word lists. Later, they used the information gain approach to perform feature extraction and the splitting of words. All three techniques achieved the highest performance for unigram words. Among them, the K-NN was shown to be the lowest-performing ML algorithm compared to the others (i.e., the K-NN's performance was 65.5%, that of the BN was 94%, and 75% was the performance of J48). According to the authors, the roundness of the training dataset has a detrimental impact on the findings [25]. [PERSON] and [PERSON] [26] investigated the effect of data preparation on text classification in news and e-mail data in English and Turkish. They accomplished the preparation through text lowercasing, stop-word deletion, tagging, and breaking. According to them, the classification includes different phases of classification and the extraction and selection of features. According to the study, English represents a non-composable language, whereas Turkish is an example of a composable language that is widely used across the globe. The text was classified using the SVM and measured using Micro-F1 algorithms. They concluded that the preparation phase is just as critical as the processes of extraction and selection of features [26]. Furthermore, they stated that stop-word cleansing is a critical phase also. This research also contends that while some preparation operations, such as lowercase modification, are required irrespective of the language or field, others must be integrated according to the language and field of the study [26]. [PERSON] and [PERSON] [27] compared the standard bag-of-words approach and artificial neural system for text classification. One of the datasets included seven distinct types of data such as sports, policy, and economics. The second dataset consisted of six distinct types, each with 600 texts. Using cleaning and stop-word procedures, they prepared the data for morphological analysis. As a classification algorithm, the NB was utilized [27]. Their findings demonstrated that stop-word cleansing and morphological analysis had little effect on the outcome. They also emphasized the significance of selecting features in the classification process and employing the Chi-Square and Knowledge Gain techniques [27]. [PERSON] [28] used quick text classification (fastText) without data preparation since data preparation is frequently time-consuming, especially in composable languages such as Turkish. Facebook's fastText word embedding-based analyzer eliminates the requirement for data preparation. The fastText algorithm was used on the Turkish text classification 3600 dataset. They tested the model using the NB, K-NN, and J48 techniques. According to the results, the best performance was attained by the Multinomial NB classifier, which scored 90.12%.