# LSRM: A New Method for Turkish Text Classification
## 4 Application of the Model
### Experimental Study, Analysis, and Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Alg Name**} & \multicolumn{4}{c}{**Original**} & \multicolumn{4}{c}{**LSRM**} \\ \cline{3-10}  & & **ACC** & **FM** & **Precision** & **Recall** & **ACC** & **FM** & **Precision** & **Recall** \\ \hline \multirow{4}{*}{TTC-3600} & NB & 0.823 & 0.823 & 0.831 & 0.823 & 0.833 & 0.834 & 0.840 & 0.833 \\  & SMO & 0.875 & 0.874 & 0.876 & 0875 & **0.891** & **0.891** & 0.892 & 0.891 \\  & KNN & 0.429 & 0.447 & 0.663 & 0.429 & 0.450 & 0.472 & 0.707 & 0.450 \\  & J48 & 0.741 & 0.739 & 0.739 & 0.742 & 0.779 & 0.778 & 0.781 & 0.779 \\  & RF & 0.872 & 0.871 & 0.874 & 0.872 & 0.884 & 0.884 & 0.888 & 0.885 \\ \hline \multirow{4}{*}{Ulakblim-v2} & NB & 0.870 & 0.869 & 0.870 & 0.870 & 0.893 & 0.893 & 0.895 & 0.893 \\  & SMO & 0.886 & 0.887 & 0.890 & 0.887 & 0.901 & 0.902 & 0.904 & 0.902 \\  & KNN & 0.356 & 0.310 & 0.634 & 0.357 & 0.368 & 0.328 & 0.712 & 0.368 \\  & J48 & 0.765 & 0.765 & 0.766 & 0.765 & 0.793 & 0.794 & 0.795 & 0.793 \\  & RF & 0.903 & 0.903 & 0.903 & 0.903 & **0.911** & **0.912** & 0.912 & 0.912 \\ \hline \multirow{4}{*}{TbTT} & NB & 0.920 & 0.921 & 0.924 & 0.920 & 0.922 & 0.922 & 0.923 & 0.923 \\  & SMO & **0.955** & **0.955** & 0.955 & 0.955 & 0.942 & 0.942 & 0.943 & 0.943 \\ \cline{1-1}  & KNN & 0.280 & 0.156 & 0.813 & 0.280 & 0.280 & 0.156 & 0.813 & 0.280 \\ \cline{1-1}  & J48 & 0.777 & 0.778 & 0.781 & 0.778 & 0.712 & 0.713 & 0.717 & 0.713 \\ \cline{1-1}  & RF & 0.947 & 0.948 & 0.950 & 0.948 & 0.925 & 0.924 & 0.925 & 0.925 \\ \hline Average & & 0.760 & 0.749 & 0.831 & 0.760 & 0.766 & 0.756 & 0.843 & 0.766 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental results for the ML algorithms. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Alg Name**} & \multicolumn{4}{c}{**Original**} & \multicolumn{4}{c}{**LSRM**} \\ \cline{3-10}  & & **ACC** & **FM** & **Precision** & **Recall** & **ACC** & **FM** & **Precision** & **Recall** \\ \hline \multirow{4}{*}{TTC-3600} & CNN & 0.886 & 0.887 & 0.889 & 0.886 & **0.900** & **0.900** & 0.901 & 0.900 \\  & LSTM & 0.865 & 0.866 & 0.874 & 0.865 & 0.876 & 0.876 & 0.881 & 0.876 \\  & GRU & 0.861 & 0.860 & 0.863 & 0.861 & 0.875 & 0.876 & 0.884 & 0.875 \\ \hline \multirow{4}{*}{Ulakblim-v2} & CNN & 0.898 & 0.897 & 0.900 & 0.898 & **0.916** & **0.917** & 0.918 & 0.917 \\  & LSTM & 0.901 & 0.902 & 0.903 & 0.902 & **0.916** & **0.917** & 0.918 & 0.917 \\  & GRU & 0.900 & 0.898 & 0.898 & 0.900 & 0.890 & 0.890 & 0.912 & 0.890 \\ \hline \multirow{4}{*}{TbTT} & CNN & 0.957 & 0.957 & 0.958 & 0.958 & 0.940 & 0.939 & 0.940 & 0.939 \\  & LSTM & 0.967 & 0.968 & 0.968 & 0.968 & 0.947 & 0.947 & 0.948 & 0.948 \\ \cline{1-1}  & GRU & **0.975** & **0.975** & 0.976 & 0.975 & 0.962 & 0.962 & 0.962 & 0.963 \\ \hline Average & & 0.912 & 0.912 & 0.914 & 0.912 & 0.913 & 0.913 & 0.918 & 0.913 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Experimental results for the DL algorithms. The generated FastText, Glove, Word2 Vec, and Doc2 Vec vectors were defined as input data for the DL algorithms used. The reason for selecting the CNN, GRU, and LSTM algorithms was that they were the predominantly used algorithms in Turkish TC studies and achieved the highest class performance [29; 30; 31]. The study used 80% and 20% of the data as test and training data during the laboratory tests, respectively. Combining WEB algorithms with the LSRM method was intended to make a more compact class representation by performing vector quantization and to ensure more precise attribute identification and, as a result, improve classification performance. Indeed, the study results indicated that the class performances derived from the vectors produced using the LSRM method were 3% higher than the average class performance rates derived from the vectors generated without using the LSRM method. The final part of the laboratory studies involved running the same datasets with TL algorithms and measuring their classification success rates.