## 4 Results of Experiments
### Results of the Experiments  
In this section, we discuss the results of our studies comparing the effects of different word embeddings on ASA. Tables 4-6 display the comparative performance of various models using different word embeddings on datasets containing Arabic text, specifically the ACRD, HARD, and LABR datasets. The models evaluated include the proposed model (Arb-MCNN-Bi), MCNN-BiGRU, MCNN, BiGRU, and various ML models. Each model's performance was measured across several metrics: precision, recall, F1-score, and accuracy. The comparative study on ASA using the ACRD reveals that DL models significantly outperform traditional ML algorithms across key performance metrics--precision, recall, F1-score, and accuracy. Traditional models such as Logistic Regression and Multinomial Naive Bayes achieved commendable results, with Logistic Regression reaching the highest accuracy among them at 92.95%. However, DL models, particularly those employing advanced feature extraction techniques like the transformer-based AraBERT, \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 LSVM & N-Gram, TF-IDF & 91.18 & 92.15 & 91.66 & 91.98 \\ PAC & N-Gram, TF-IDF & 91.37 & 91.64 & 91.50 & 91.51 \\ MNB & N-Gram, TF-IDF & 92.45 & 92.21 & 92.32 & 92.34 \\ LR & N-Gram, TF-IDF & 92.83 & 92.81 & 92.82 & 92.95 \\ DT & N-Gram, TF-IDF & 83.47 & 83.35 & 83.40 & 83.59 \\ KNN & N-Gram, TF-IDF & 90.45 & 89.65 & 90.05 & 90.42 \\ RF & N-Gram, TF-IDF & 91.73 & 91.86 & 91.79 & 92.53 \\ \hline \multirow{3}{*}{MCNN} & Word2 Vec(SG) & 95.43 & 95.28 & 95.35 & 95.43 \\  & FastText(SG) & 96.11 & 96.13 & 96.12 & 96.14 \\ \cline{1-1}  & AraBERT & 96.76 & 96.55 & 96.65 & 96.82 \\ \hline \multirow{3}{*}{BiLSTM} & Word2 Vec(SG) & 95.14 & 95.17 & 95.15 & 95.22 \\  & FastText(SG) & 95.61 & 95.68 & 95.64 & 95.72 \\ \cline{1-1}  & AraBERT & 96.10 & 95.98 & 96.04 & 96.08 \\ \hline \multirow{3}{*}{BiGRU} & Word2 Vec(SG) & 95.21 & 95.16 & 95.18 & 95.24 \\ \cline{1-1}  & FastText(SG) & 95.23 & 95.20 & 95.21 & 95.25 \\ \cline{1-1}  & AraBERT & 96.13 & 96.01 & 96.06 & 96.11 \\ \hline Arb-MCNN-Bi & AraBERT & 96.87 & 96.90 & 96.89 & 96.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance Comparison of Various Models for Arabic Sentiment Analysis Using Different Feature Extraction Techniques on ACRD. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **ACRD** & **HARD** & **LABR** \\ \hline Embedding model & AraBERT, & AraBERT, & AraBERT, \\  & FastText,Word2 VC & FastText,Word2 VC & FastText,Word2 VC \\ Conv1D1 (Filter,Kernelsize) & (128, 3) & (128, 3) & (128, 3) \\ Conv1D2 (Filter,Kernelsize) & (128, 4) & (128, 4) & (128, 4) \\ Conv1D3 (Filter,Kernelsize) & (128, 5) & (128, 5) & (128, 5) \\ Batch size & 128 & 128 & 128 \\ Learning rate & 0.00001 & 0.00001 & 0.00001 \\ Max. length & 64 & 64 & 64 \\ Optimizer & Adam & Adam & Adam \\ DropoutID & 0.1, 0.15, 0.25 & 0.1, 0.15, 0.25 & 0.1, 0.15, 0.25 \\ BiGRU & 128 & 128 & 128 \\ DropoutBi & 0.5 & 0.5 & 0.5 \\ Activation function & ReLU & ReLU & ReLU \\ Output function & Softmax & Softmax & Softmax \\ Epochs & 25 & 15 & 20 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Hyperparameters of the Proposed Model. demonstrated superior performance. The Arb-MCNN-Bi hybrid model, combined with AraBERT, emerged as the top performer, achieving an impressive accuracy of 96.92%, along with high precision, recall, and F1-score values. This underscores the efficacy of leveraging sophisticated deep-learning architectures and transformer-based models for more accurate sentiment analysis in Arabic. The performance analysis of various models for ASA on the HARD dataset shows that DL models consistently outperform traditional ML algorithms.