# Machine Learning-Based Text Classification Comparison: Turkish Language Context
## 2 Literature Review
### Text Classification in Turkish Context
Accordingly, the author concluded that the fastText algorithm is substantially superior compared to other methods in terms of consistency without the need for data preparation [27]. [PERSON] et al. [29] used Deep Learning (DL) to perform sentiment analysis on public Facebook data acquired from Turkish user profiles. With text representations, recurrent neural systems obtained the highest accuracy, 91.6%. [PERSON] et al. [30] suggested a DL-based classification of news texts using the Doc2 vec word-based approach on the Turkish text classification 3600 dataset. DL-based Convolutional Neural Networks (CNN) and classic ML methods such as the NB, SVM, RF, and Gauss NB algorithms were employed as classification techniques. In the suggested model, the maximum result was reached as 94.17% in the Turkish sample compared to 96.41% in the English sample in the classifications performed by CNN [30]. [PERSON] et al. [31] employed DL to perform question classification in Turkish. They used three different DL algorithms (Gated Recurrent Unit, LSTM, and CNN). They also used the word2 vec methodology. Word2 vec strategies had a major effect on the prediction performance utilizing multiple DL algorithms, which achieved an accuracy of 93.7% [31]. [PERSON] [32] used text classification tools to analyze 7731 tweets from 13 prominent Turkish economists. The classification findings were then compared to four popular ML algorithms (the SVM, NB, LR, and integration of LR with the SVM). The results revealed that the success of a text classification issue is related to the feature extraction techniques and that the SVM outperforms other ML methods using unigram feature maps. The integration approach of the SVM with LR generated the best results (82.9%) [32]. [PERSON] et al. [23] deployed ML to classify customers' concerns regarding packaged food goods expressed in Turkish. The class of concern was determined using the TF-IDF and word2 vec feature extraction algorithms. The results of the LR, NB, K-NN, SVM, RF, and Extreme Gradient Boosting classifiers were compared. The strongest technique was Extreme Gradient Boosting with an TF-IDF weighted value, which reaches an 86% F-measure score [23]. When compared to the TF-IDF method, word2 vec-based ML performed poorly in terms of the F-measure. Furthermore, TF-IDF-based ML provides more accurate predictions on the optimized feature subsets determined by the Chi-Square approach, which, when conducted on TF-IDF features, raises the F-measure from 86% to 88% in Extreme Gradient Boosting [23]. [PERSON] [33] introduced a similarity metric for classification that can be utilized well for word vectors and classification techniques such as the K-NN and k-means methods. The suggested metric is validated against certain global datasets in English and Turkish. The suggested metric might be employed in any applicable method or model for data acquisition and text classification. [PERSON] and [PERSON] [22] performed a content-based SMS classification utilizing ML and DL approaches to filter out undesirable texts in Turkish. The features were analyzed using DL and ML and the results were compared. As a consequence, the CNN technique was discovered to be the best successful technique, with a 99.86% accuracy rate in classification. [PERSON] and [PERSON] [21] proposed a technique and considerations for improving text categorization effectiveness with ML. ML methods and state-of-the-art preparation text models were used to assess two publicly available Turkish news datasets. They used a variety of ML techniques, including the NB, LR, K-NN, SVM, and RF techniques. They also used a BERT model that was particularly trained for classifying Turkish text. The results demonstrated that the technique outperformed earlier F1-score-based news classification experiments and achieved 96% accuracy [21]. [PERSON] [20] presented a data distribution algorithm that addresses the data imbalance issue to improve text categorization success.