## 5 Conclusions  
This study addresses the problem of sentiment analysis for Arabic text. The performance of the Arabic sentiment analysis system was examined in relation to the use of ensemble stacking models based on CNN, a hybrid CNN-LSTM model, and a hybrid CNN-GRU model. Additionally, the ensemble stacking models have made important contributions to increasing NLP accuracy. In order to improve the model's performance in forecasting Arabic sentiment analysis, we suggested an optimal ensemble staking model that includes three pre-trained models: deep layers of CNN, hybrid CNN-LSTM, and hybrid CNN-GRU, together with a meta-learner SVM. Different layers are included in the DeepCNN model: the flatten layer, fully connected layer, output layer, two MaxPooling layers, three CNN layers, and the global MaxPooling layer. The embedding layer, CNN and MaxPooling layers, long short-term memory (LSTM), fully linked layer, and output layer are all components of the hybrid CNN-LSTM model. The embedding layer, CNN and MaxPooling layers, the gated recurrent unit (GRU), the fully linked layer, and the output layer are all components of the hybrid CNN-GRU model. To extract features for DL models, CBOW and the skip-gram models with 300 dimensions word embedding were utilized. The performance of the proposed model is evaluated against DeepCNN, hybrid CNN-LSTM, hybrid CNN-GRU, and conventional ML algorithms. The findings demonstrate that, when compared to other models, the suggested ensemble model has the best performance for each dataset. The proposed model with CBOW word embedding has the highest accuracy of 92.12%, 95.81%, and 81.4% for Main-AHS, Sub-AHS, and ASTD, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Paper** & **Method** & **Dataset** & **Performance** \\ \hline
[9] & CNN-LSTM & ASTD &
\begin{tabular}{c} 65.05\% for accuracy \\ 64.46\% for f1-score \\ \end{tabular} \\ \hline
[17] & CNN-LSTM & ASTD &
\begin{tabular}{c} 66\% for accuracy, \\ 62\% for f1-score \\ 66\% for recall \\ \end{tabular} \\ \hline
[7] & CNN-LSTM &
\begin{tabular}{c} Main-AHS \\ \end{tabular} & 88.1\% for accuracy \\ \hline
[18] & CNN & ASTD & 79.07\% for accuracy \\ \hline
[21] & ensemble model using voting based on CNN-LSTM & ASTD & 64.46\% for f1-score \\ \hline
[63] & Bi-LSTM & ASTD &
\begin{tabular}{c} 79.25\% for Accuracy \\ 76.83 of F1-score \\ \end{tabular} \\ \hline
[9] & CNN & Main-AHS &
\begin{tabular}{c} 92.61\% for Accuracy \\ 86.03\% of f1-score \\ \end{tabular} \\ \hline
[9] & CNN &
\begin{tabular}{c} Main-AHS \\ \end{tabular} & 92\% for accuracy \\ \hline
[9] &
\begin{tabular}{c} 62.12\% for accuracy \\ 91.31\% for precision \\ 91.27\% of f1-score \\ \end{tabular} \\ \hline
[9] & \begin{tabular}{c} Stacking SVM based on integrated \\ DeepCNN, CNN-LSTM and CNN-GRU \\ \end{tabular} & Sub-AHS & \
\begin{tabular}{c} 95.81\% for accuracy \\ 96.06\% for precision \\ 95.81\% for recall \\ 95.67\% for f1-score \\ \end{tabular} \\ \hline ASTD & ASTD &
\begin{tabular}{c} 81.4\% for accuracy \\ 80.69\% of precision \\ 81.4\% for recall \\ 80\% for f1-score \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of previous studies and the proposed models.  
In future work, the word sense in Arabic is critical and expected to improve performance [67], and we will consider this limitation in the future.  
Methodology, H.S.; Validation, H.S.; Visualization, H.S. and S.M.; Writing--review & editing, H.S., S.M., S.E.-S., L.A.G. and A.O.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All datasets used to support the findings of this study are available from the direct link in the dataset citations.