## 5 Conclusions  
Our research addresses the significant challenge of performing sentiment analysis on Arabic text, which is complicated by the language's unique characteristics. We propose a hybrid model combining AraBERT, MCNN, and BiGRU networks to analyze Arabic sentiments effectively. Our model leverages AraBERT for rich contextual understanding, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Feature Extraction**} & \multicolumn{4}{c}{**Performance**} \\  & & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline \hline  & N-Gram, TF-IDF & 78.12 & 78.02 & 78.07 & 78.14 \\  & N-Gram, TF-IDF & 78.45 & 78.55 & 78.50 & 78.78 \\  & N-Gram, TF-IDF & 80.11 & 80.14 & 80.12 & 80.23 \\  & N-Gram, TF-IDF & 81.02 & 81.21 & 80.11 & 80.18 \\  & T & N-Gram, TF-IDF & 70.24 & 71.05 & 70.64 & 71.12 \\  & N-Gram, TF-IDF & 71.18 & 71.15 & 71.16 & 71.24 \\  & RF & N-Gram, TF-IDF & 79.11 & 79.14 & 79.12 & 79.25 \\ \hline \multirow{3}{*}{MCNN} & Word2 Vec(SG) & 81.15 & 81.08 & 81.11 & 81.24 \\  & FastText(SG) & 81.55 & 81.75 & 81.65 & 81.88 \\  & AraBERT & 82.45 & 82.56 & 82.50 & 82.74 \\ \hline \multirow{3}{*}{BiLSTM} & Word2 Vec(SG) & 82.14 & 82.75 & 82.44 & 83.02 \\  & FastText(SG) & 82.87 & 83.17 & 83.02 & 83.48 \\  & AraBERT & 84.45 & 84.46 & 84.45 & 84.57 \\ \hline \multirow{3}{*}{BiGRU} & Word2 Vec(SG) & 82.19 & 81.16 & 81.67 & 82.14 \\  & FastText(SG) & 83.78 & 83.01 & 83.39 & 84.28 \\  & AraBERT & 85.92 & 85.74 & 85.83 & 85.86 \\ \hline Arb-MCNN-Bi & AraBERT & 92.37 & 92.26 & 92.31 & 92.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance Comparison of Various Models for Arabic Sentiment Analysis Using Different Feature Extraction Techniques on the LABR Dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **RF.** & **Model** & **Recall** & **Precision** & **F1-Score** & **Accuracy** \\ \hline \multirow{8}{*}{HARD} & [PERSON] et al. [17] & ARABERT & - & - & - & 96.20 \\  & [PERSON] et al. [16] & CNN-LSTM & 90.36 & 90.47 & 90.42 & 90.38 \\  & [PERSON], et al. [11] & CNN & 93.23 & 93.23 & 93.23 & 93.58 \\  & [PERSON] et al. [21] & hULMonA & - & - & 95.70 & 95.70 \\  & [PERSON] et al. [19] & BERT & - & - & - & 95.00 \\  & Our model & Arb-MCNN-Bi & 96.56 & 96.57 & 96.56 & 96.68 \\ \hline \multirow{3}{*}{LABR} & [PERSON] et al. [17] & ARABERT & - & - & - & 86.70 \\  & [PERSON] et al. [19] & BERT & - & - & - & 87.00 \\ \cline{1-1}  & [PERSON] et al. [15] & CNN-LSTM & - & - & - & 81.14 \\ \cline{1-1}  & Our model & Arb-MCNN-Bi & 92.26 & 92.37 & 92.31 & 92.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the Arb-MCNN-Bi Method with State-of-the-Art Methods on HARD and LABR Dataset. MCNN for enhanced feature extraction, and BiGRU for capturing long-term dependencies, achieving high accuracy rates across multiple Arabic datasets. Moreover, we compared the proposed model with various ML and DL algorithms, using advanced NLP techniques, such as TF-IDF, n-gram, Word2 Vec (Skip-grams), and fastText (Skip-grams), across three Arabic datasets: ACRD, LABR, and HARD. The Arb-MCNN-Bi model achieved remarkable accuracies of 96.92%, 96.68%, and 92.93% on the ACRD, HARD, and LABR datasets, respectively. These results highlight the effectiveness of our approach in analyzing Arabic text data. Through a comprehensive comparative analysis, our Arb-MCNN-Bi model has demonstrated superior performance and robustness compared to various traditional ML and DL algorithms. This work contributes substantially to the field of ASA, providing a robust tool for businesses to better understand and respond to customer feedback in the Arabic language. Conceptualization, H.A. and F.Z. ; methodology H.A. and F.Z. ; software, H.A. ; validation, H.A., A.M. and F.Z. ; formal analysis, H.A. and F.Z. ; investigation, H.A. ; resources, H.A. and F.Z. ; data curation, H.A. and F.Z. ; writing--original draft preparation, H.A. ; writing--review and editing, H.A., A.M. and F.Z. ; visualization, H.A. ; supervision, F.Z. ; project administration, H.A.