# Arabic Hate Speech Detection Using Deep Recurrent Neural Networks
## 4 Experimental Results and Discussion
### Multiclass Hate Speech Classification of Arabic Comments  
The third experiment was based on seven classes, namely comments against religion, racist comments, comments against gender equality, violent/offensive comments, insulting/bullying comments, normal positive comments, and normal negative comments. To the author's knowledge, this may be the first study targeting seven classes of Arabic comments. Table 8 shows the results of using the DT, MLP, NB, and LR classifiers. It can be seen from Table 8 that the highest accuracy (34.761%) was achieved using DT; this was still a very low percentage. The precision, recall, and F1 score were the highest for DT as well, with values of 0.35, 0.35, and 0.34, respectively. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline DT & 56.192\% & 0.56 & 0.56 & 0.56 \\ MLP & 47.505\% & 0.51 & 0.48 & 0.49 \\ NB & 54.710\% & 0.53 & 0.54 & 0.5 \\ LR & 51.449\% & 0.48 & 0.54 & 0.48 \\ RF & 50.881\% & 0.54 & 0.52 & 0.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of experimental results using the typical classification methods for the three classes of Arabic comments. Figure 5: Confusion matrix of the experimental results using the proposed deep recurrent neural network model-based classification method for three classes of Arabic comments. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Iterations** & **Train Loss** & **Train Acc.** & **Val. Loss** & **Val. Acc.** \\ \hline
10 & 0.0576 & 98.26\% & 1.2928 & 77.00\% \\
25 & 0.0257 & 99.31\% & 2.1474 & 69.61\% \\
50 & 0.2016 & 92.48\% & 0.4729 & 84.80\% \\ Best & 0.1391 & 95.38\% & 0.4148 & 86.40\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of experimental results using the typical classification methods for three classes of Arabic comments. A repeat of the same experiment using the proposed deep RNN model (DRNN-2) showed a significant improvement in the classification of the seven classes of Arabic comments, as shown in Table 9. Again, the choice to show the results of DRNN-2 for these experiments was because it produced a significant improvement in accuracy over DRNN-1. The training accuracy increased to 84.14%, with a training loss of 0.4752 and a validation loss and validation accuracy of 1.6282 and 58.26, respectively. It can be observed that the proposed model showed better results than those listed in Table 8 as well as those listed in Table 1.  
Figure 6 shows the confusion matrix for the classification of Arabic comments into seven classes. The diagonal squares indicate that the confusion was minimal when it came to normal positive comments; medium when it came to racist, violent/offensive, and bullying comments; and worse when it came to negative normal, religious, and gender inequality comments. A complete cloud-based system will be designed with various smart functions, such as a system to alert the authorities, a function to produce tangible reports, one to produce various reports according to need, and a human-computer interface in order to focus on certain aspects in certain situations. The system prototype is expected to be developed towards the end of this project, and publication in a Scopus-indexed or ISI-indexed journal may result from the complete system. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline DT & 34.761\% & 0.35 & 0.35 & 0.34 \\ MLP & 17.884\% & 0.22 & 0.18 & 0.19 \\ NB & 24.175\% & 0.24 & 0.22 & 0.16 \\ LR & 17.692\% & 0.16 & 0.29 & 0.21 \\ RF & 21.762\% & 0.23 & 0.24 & 0.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of experimental results using the typical classification methods for seven classes of Arabic comments.