# Machine Learning-Based Text Classification Comparison: Turkish Language Context
## 6 Conclusions and Future Work  
Text classification has had a significant influence on domains such as news categorization, the detection of spam content, and semantic analysis. The classification of Turkish text is the focus of this work since only a few studies have been conducted in this context. We utilized data obtained from letters of request that came to an institution to evaluate the proposed techniques. Five distinct classification techniques were used in this work, and the findings were thoroughly reviewed. The techniques were applied to the raw data and the preprocessed data to investigate the impacts of data preparation. The results then were compared utilizing accuracy, training time, F1-score, and recall values. Data were normalized, morphologically analyzed, processed through word stop, and simplified using the k-means technique. The data were trained using the SVM, NB, LTSM, RF, and LR techniques. The performance of the various techniques was analyzed after and before data preparation.  
The LTSM was found to be the most effective technique in terms of accuracy and training time. The findings show that normalized data, as well as coherence between the categories' number and the number of training sets, are significant variables influencing the techniques' performance. Additionally, the techniques performed better once the number of categories in the dataset was reduced. The comparison also demonstrates that of all the preparation stages, the simplifying phase has the biggest influence on the results. The TF-IDF approach utilized in the feature extraction stage, on the other hand, has the biggest impact on the results. Overall, the findings of this study achieved 84% performance accuracy, which is considerably higher than those of previously proposed text classification solutions. Furthermore, the text categorization approach utilized in this work applies to data in languages other than Turkish. However, in the morphological analysis stage of a comparable study, one must employ a strategy appropriate to their languages.  
In NLP jobs, it is critical to analyze data for future research and analyses. The fact that the data in these jobs are based on natural languages (from humans) may lead to excess data filth. Furthermore, data utilized in algorithms for supervised learning must be classified repeatedly for a trained model to produce accurate findings. More efficient outcomes can be obtained by training the model using a classifier more consistently. That is, once the dataset is expanded with fresh data, an LSTM technique with higher accuracy may be provided. With data cleansing, it is also feasible to decrease the time required for the preparation.  
Conceptualization, A.E.E. and A.E.T.; methodology, Y.I.A.; software, A.E.E.; validation, Y.I.A. and A.E.T.; writing--original draft preparation, A.E.E.; writing--review and editing, Y.I.A.; supervision, A.E.T. All authors contributed to the study conception and design. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data available on request due to restrictions on privacy or ethical.  
The authors declare no conflict of interest.