# Developing Analytical Tools for Arabic Sentiment Analysis of COVID-19 Data
## 3 Research Methodology
### Lexicon Construction
#### 3.1.2 Constructing a Lexicon Using the Trained Data
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature** & **Classifier** & **Accuracy** & **Precision** & **Recall** & **F-Score** \\ \hline \multirow{8}{*}{\(\mathbb{\Xi}\)} & SVM & 88.46 & 88.55 & 88.46 & 88.37 \\  & GNB & 59.34 & 73.46 & 59.34 & 61.45 \\  & MNB & 78.02 & 81.88 & 78.02 & 76.51 \\  & BNB & 80.22 & 80.85 & 80.22 & 79.70 \\  & DT & 87.91 & 87.90 & 87.91 & 87.89 \\  & RF & 86.81 & 86.67 & 86.81 & 86.64 \\  & LR & 85.71 & 85.96 & 85.71 & 85.41 \\  & KNN & 83.52 & 83.28 & 83.52 & 83.19 \\ \hline \multirow{8}{*}{\(\mathbb{\Xi}_{\alpha}^{\text{t}}\)} & SVM & 90.11 & 90.45 & 90.11 & 89.92 \\  & GNB & 59.34 & 73.46 & 59.34 & 61.45 \\  & MNB & 86.26 & 86.95 & 86.26 & 86.05 \\  & BNB & 80.77 & 81.61 & 80.77 & 80.22 \\  & DT & 85.16 & 85.09 & 85.16 & 85.01 \\  & RF & 89.01 & 89.20 & 89.01 & 88.84 \\  & LR & 90.11 & 90.45 & 90.11 & 89.92 \\  & KNN & 84.07 & 84.56 & 84.07 & 83.68 \\ \hline \multirow{8}{*}{\(\mathbb{\Xi}\)} & SVM & 89.56 & 89.97 & 89.56 & 89.43 \\  & GNB & 59.34 & 73.46 & 59.34 & 61.45 \\  & MNB & 82.97 & 85.33 & 82.97 & 81.86 \\  & BNB & 79.12 & 79.87 & 79.12 & 78.52 \\  & DT & 84.62 & 84.65 & 84.62 & 84.43 \\  & RF & 88.46 & 88.48 & 88.46 & 88.33 \\  & LR & 87.91 & 88.55 & 87.91 & 87.68 \\  & KNN & 82.97 & 83.18 & 82.97 & 82.66 \\ \hline \multirow{8}{*}{\(\mathbb{\Xi}\)} & SVM & 92.86 & 92.93 & 92.86 & 92.86 \\  & GNB & 80.52 & 81.25 & 80.52 & 80.28 \\  & MNB & 88.31 & 88.97 & 88.31 & 88.45 \\  & BNB & 85.71 & 85.69 & 85.71 & 85.64 \\  & DT & 86.36 & 86.5 & 86.36 & 86.41 \\  & RF & 89.61 & 89.67 & 89.61 & 89.62 \\  & LR & 90.91 & 90.92 & 90.91 & 90.89 \\  & KNN & 88.31 & 88.51 & 88.31 & 88.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Performance of ML classifiers for a polarity-based feature set. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature** & **Classifier** & **Accuracy** & **Precision** & **Recall** & **F-Score** \\ \hline \multirow{8}{*}{\(\mathbb{R}^{2}\)} & SVM & 89.01 & 89.51 & 89.01 & 88.86 \\  & GNB & 59.89 & 74.43 & 59.89 & 62.03 \\  & MNB & 87.91 & 88.13 & 87.91 & 87.74 \\  & BNB & 79.67 & 80.6 & 79.67 & 79.11 \\  & DT & 81.87 & 81.74 & 81.87 & 81.71 \\  & RF & 87.91 & 88.04 & 87.91 & 87.69 \\  & LR & 87.36 & 88.11 & 87.36 & 87.11 \\  & KNN & 73.08 & 79.79 & 73.08 & 69.92 \\ \hline \multirow{8}{*}{\(\mathbb{R}^{2}\)} & SVM & 90.26 & 90.8 & 90.26 & 90.31 \\  & GNB & 79.87 & 80.84 & 79.87 & 79.68 \\  & MNB & 85.71 & 86.16 & 85.71 & 85.80 \\  & BNB & 85.71 & 85.66 & 85.71 & 85.65 \\  & DT & 86.36 & 86.35 & 86.36 & 86.35 \\  & RF & 88.96 & 88.99 & 88.96 & 88.97 \\  & LR & 87.66 & 88.35 & 87.66 & 87.75 \\  & KNN & 68.83 & 74.54 & 68.83 & 67.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Performance of ML classifiers for negation-based feature set. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature** & **Classifier** & **Accuracy** & **Precision** & **Recall** & **F-Score** \\ \hline \multirow{8}{*}{\(\mathbb{R}^{2}\)} & SVM & 89.01 & 89.51 & 89.01 & 88.86 \\  & GNB & 59.89 & 74.43 & 59.89 & 62.03 \\  & MNB & 87.91 & 88.13 & 87.91 & 87.74 \\  & BNB & 79.67 & 80.6 & 79.67 & 79.11 \\  & DT & 81.87 & 81.74 & 81.87 & 81.71 \\  & RF & 87.91 & 88.04 & 87.91 & 87.69 \\  & LR & 87.36 & 88.11 & 87.36 & 87.11 \\  & KNN & 73.08 & 79.79 & 73.08 & 69.92 \\ \hline \multirow{8}{*}{\(\mathbb{R}^{2}\)} & SVM & 90.26 & 90.8 & 90.26 & 90.31 \\  & GNB & 79.87 & 80.84 & 79.87 & 79.68 \\  & MNB & 85.71 & 86.16 & 85.71 & 85.80 \\  & BNB & 85.71 & 85.66 & 85.71 & 85.65 \\  & DT & 86.36 & 86.35 & 86.36 & 86.35 \\  & RF & 88.96 & 88.99 & 88.96 & 88.97 \\  & LR & 87.66 & 88.35 & 87.66 & 87.75 \\  & KNN & 68.83 & 74.54 & 68.83 & 67.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Performance of ML classifiers for emojis-based feature set. Table 16 shows the results of the experiments (four performance parameters, Accuracy, Precision, Recall, and F-score) for all eight machine learning classification algorithms in predicting sentiments of tweets using all previously feature sets.