# Machine Learning-Based Text Classification Comparison: Turkish Language Context
## 2 Literature Review
### Natural Language Processing  
NLP is an AI discipline that deals with the processing of human language in a computer-readable format. NLP, which was designed to help computers comprehend the language humans use to communicate, has become very common [13]. The ease with which people's speech may now be accessed via social media and the proliferation of communication outlets such as radio and television have enlarged the usage of NLP. NLP is required in text classification to analyze user data. Building a process by making meaning out of text data is a difficult step. NLP is practiced in several stages. The sentences, firstly, are divided into smaller sections called tokens in lexical analysis. The structure of the sentence or syntax of all tokens is then considered in the syntactic analysis process [12]. It is tested to see whether its syntax is correct. The interpretations of phrases are inferred \begin{table}
\begin{tabular}{c c c} \hline \hline
**Study** & **Technique/Algorithm Used** & **Findings** \\ \hline
[24] & SVM, NB, RF & The NB is the best for author discovery; the SVN is the best for race and genre identification \\ \hline
[25] & DT-J48, K-NN, Bayesian Probabilistic classifiers, N-gram method & K-NN performance achieved 65.5\%, BN achieved 94\%, and J48 achieved 75\% \\ \hline
[26] & SVM, Micro-F1 & The preparation phase is just as critical as the processes of the extraction and selection of features \\ \hline
[27] & Bag-of-words approach, artificial neural system & Stop-word cleansing and morphological analysis had little effect on the outcome \\ \hline
[28] & FastText tested using NB, K-NN, J48 & Multinomial NB classifier achieved the best at 90.12\% \\ \hline
[29] & DL techniques & Recurrent neural systems obtained the highest accuracy of 91.6\% \\ \hline
[30] & DL, ML techniques (NB, SVM, RF, and Gauss NB) & 94.17\% in the Turkish sample compared to 96.41\% in the English sample in the classifications performed by CNN \\ \hline
[31] & DL techniques (Gated Recurrent Unit, LSTM, CNN) & DL algorithms achieved an accuracy of 93.7\% on the question dataset \\ \hline
[32] & SVM, NB, LR, and integration LR with SVM & The integration approach of the SVM with LR generated the best results (82.9\%) \\ \hline
[23] & LR, NB, K-NN, SVM, RF applied on TF-IDF and word2 vec & Extreme Gradient Boosting with an TF-IDF weighted value scored the best (86\%) F-measure score \\ \hline
[33] & Proposed a similarity metric that can be used for K-NN and k-means & The suggested metric might be employed in any applicable method or model for data acquisition and text classification \\ \hline
[22] & ML (NB, RF, SVM, multilayer perceptron, Random Subspace, LR, K-NN, DL (CNN and LSTM)) & CNN scored the best with a 99.86\% accuracy rate \\ \hline
[21] & NB, LR, K-NN, SVM, RF & The new technique outperformed earlier F1-score-based news classification experiments and achieved 96.00\% accuracy. \\ \hline
[20] & LSTM & A new data distribution methodology \\ \hline This study & k-means technique, TF-IDF, SVM, NB, LTSM, RF, LR & LTSM was found to be the most effective technique in terms of accuracy, and data preparation is important for the overall performance of the algorithm used \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Recent studies on ML Turkish text classification. using the preceding procedures in semantic analysis. The NLP stages are completed after turning the data into output data. In this work, a morphology analysis tool, a key element of NLP, was employed in data preparation. Turkish morphology analysis was carried out using the Zemberek library. Zemberek performs parsing with root dictionary-based parses. The library conducts parsing by calculating the probability of the root. The library initially scans the binary root record stored before appending the relevant sentences to the root. The origin of a term in Turkish can take several forms. Various changes were added to the tree for such assertions and distorted terms.