## 4 Discussion  
Table 12 illustrates the summary of the state-of-the-art on used datasets. Furthermore, Table 13 compares the proposed approach with the state-of-the-art for each of the four datasets. [PERSON] et al. [19], outperformed the state-of-the-art result on [PERSON] scoring a 94% F1-score. Our proposed approach that combined the USE and word2 vec features and two-level stacking ensemble surpassed that result by 3.1%, indicating that USE contributed to the classification performance. For the same dataset, it can be seen from Table 9 that unlike the proposed stacking ensembles listed in Table 7, majority voting improves the performance of the base classifiers in only one combination. The results of the SemEval-2019 competition for Task 5 HatEval can be found in [37]. Even though the proposed system outperformed the highest-ranked system, which employed SVM with sentence embeddings features, the performance improvement is not as pronounced as the one on the [PERSON] dataset. For the ZeerakW dataset, the best result for binary classification was achieved by [PERSON] et al. [18] using ensemble neural networks with various weight initialization along with word embedding features. To the best of our knowledge, there are no published results on binary hate speech classification using the COVID-HATE dataset. We observed that LR and SVM were the most successful models among all the used models on the test set in terms of the F1-score. As shown in Table 7 in the proposed stacking ensemble approach, the two best combinations have the LR and SVM in them (SVM, LR, XGB and SVM, LR, E-tree). One can argue that these combinations were the best because they used LR as a Meta-Level classifier; however, LR was also the best in the other two approaches that did not utilize a Meta-Level classifier. In Table 8, LR achieved \begin{table}
\begin{tabular}{c c c c c} \hline
**Base-Classifiers Combination** & **HatEval** & **Davidson** & **COVID-HATE** & **ZeerakW** \\ \hline SVM, LR, XGB & 0.5910 & **0.9517** & 0.7036 & 0.6657 \\ KNN, SVM, NB & 0.5746 & 0.9434 & 0.6623 & 0.6806 \\ LR, NB, RF & 0.5804 & 0.9412 & **0.7047** & 0.6754 \\ KNN, LR, NB & 0.5873 & 0.9437 & 0.7046 & **0.6815** \\ SVM, LR, E-tree & **0.5943** & 0.9491 & 0.6859 & 0.6472 \\ \hline \end{tabular}
\end{table} 
Table 10: F1-score of the standard stacking on test sets. Bold entries show the highest performance for each classifier on each dataset. \begin{table}
\begin{tabular}{c c c c c} \hline
**Approaches** & **HatEval** & **Davidson** & **COVID-HATE** & **ZeerakW** \\ \hline Proposed Stacking & **0.6551** & **0.9713** & **0.7301** & **0.7392** \\ Standard Stacking & 0.5943 & 0.9517 & 0.7047 & 0.6815 \\ Single classifiers & 0.6307 & 0.9530 & 0.7208 & 0.7179 \\ Majority voting & 0.6407 & 0.9552 & 0.7246 & 0.7221 \\ \hline \end{tabular}
\end{table} 
Table 11: Performance of the four hate speech recognition systems in terms of F1-score on tests sets of the four datasets. Bold entries show the highest performance for each classifier on each dataset. \begin{table}
\begin{tabular}{c c c c c} \hline
**State-of-the-Art** & **Dataset** & **F1-Score** & **Model** & **Features** \\ \hline [PERSON] et al. [9] & HatEval & 65.1\% & SVM & USE \\ [PERSON] et al. [19] & [PERSON] & 94.0\% & CNN, GRU (Ensemble) & word2 vec \\ [PERSON] et al. [18] & ZeerakW & 77.8\% & Neural networks (Ensemble) & word2 vec \\ \hline \end{tabular}
\end{table} 
Table 12: Summary of the state-of-the-art approaches on used datasets. the best F1-score on two datasets: HatEval and ZeerakW, among Base-Level classifiers. Furthermore, in the majority voting, LR was in the combinations that achieved the highest performed F1-score on each dataset, (SVM, LR, XGB), (KNN, LR, NB), and (SVM, LR, E-tree) as shown in Table 9.  
When the results of the standard stacking approach from Table 10 are compared with the results of the proposed stacking approach, it is seen that the proposed approach outperformed the standard stacking approach on all datasets in terms of the F1-score.