## 5 Discussion
### Arabic Sentiment Analysis  
For this application, the proposed ArCAR system was evaluated using BRAD and HARD datasets for binary and multiclass problems. Fortunately, the BRAD and HARD datasets for the binary-class problem have balanced positive and negative review datasets. As shown in Table 9, the best performance is achieved using HARD-balance datasets (i.e., binary-class problem) with overall accuracy, recall, precision, and F1-score of 93.58%, 93.23%, 93.26%, and 93.23%, respectively. Using the BRAD dataset, the ArCAD systems achieved better performance for the binary-class problem by 4.33%, 14.2%, 13.09%, and 14.71% in terms of overall accuracy, F1-score, recall, and precision, respectively. Similarly, using the HARD-balance dataset, the ArCAR system achieves better performance in terms of recall, precision, and F1-score by 12.25%, 10.93%, and 11.60%, respectively. Despite the unbalanced remedies used for training and testing purposes, the ArCAR system always achieves better performance using a balanced dataset (i.e., binary-class problem). With the balanced datasets, the evaluation performance is more stable over all evaluation metrics. As depicted in Table 9 and Figure 5, the proposed ArCAR system shows its reliability and feasibility to handle the BRAD and HARD datasets with binary and multiclass problems with more than 500 thousand Arabic article reviews. As listed in Tables 9 and 10, the ArCAR system achieves encouraging evaluation results for Arabic sentiment reviews and requires less than 0.105 msec for each Arabic article recognition. For computation time cost, ArCAR systems need less recognition time in the case of sentiment analysis compared with Arabic document texts. This is because the size of each Arabic text document is larger than that of a single Arabic review. For performance comparison of the proposed ArCAR system performance with the latest work, the comparison results regarding the evaluation metrics are summarized in Table 13. The proposed ArCAR system for Arabic sentiment analysis using character-level representation shows promising comparable recognition results with the latest studies using different ML approaches [13; 37]. For example, using the HARD balanced dataset, the proposed ArCAR system outperforms the recognition system using SVM presented in [54] by 15.23%. Meanwhile, the ArCAR system performs better evaluation performance than the random forest presented in [54] by 5.58% using the multiclass HARD dataset. Although the proposed ArCAR system showed promising Arabic text recognition results for documents recognition and sentiment analysis, some drawbacks and limitations are noted. First, the sizes of training and testing annotated Arabic text documents and sentiments are still limited. Thus, the data augmentation strategy may help to improve the performance of the ArCAR system. Second, even though some remedies are utilized to avoid system bias due to unbalanced datasets, it seems insufficient since deep learning models perform well with balanced datasets. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Method** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline \multirow{3}{*}{BRAD Binary-class} & SVM & - & - & 80.0 & 82.0 \\  & [PERSON] et al. [46] & - & - & 81.45 & 81.46 \\  & The proposed ArCAR System & 81.48 & 81.44 & 81.45 & 81.46 \\  & SVM & - & - & 71.0 & 78.0 \\  & [PERSON] et al. [46] & - & - & 71.0 & 78.0 \\  & The proposed ArCAR System & 66.77 & 68.35 & 67.25 & 77.13 \\  & SVM & - & - & 81.0 & 78.0 \\  & The proposed ArCAR System & 93.26 & 93.23 & 93.23 & 93.23 \\  & Random Forest & - & - & 51.0 & 88.0 \\  & [PERSON] et al. [47] & - & - & 51.0 & 88.0 \\  & The proposed ArCAR System & 82.33 & 80.98 & 81.63 & 93.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Comparison Evaluation Results with the Latest Works for Arabic Sentiment Analysis. Future plans to improve the performance of the proposed ArCAR system are addressed as follows.