# 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}{*}{**KNN**} & SVM & 89.01 & 89.76 & 89.01 & 88.82 \\  & GNB & 66.48 & 78.03 & 66.48 & 68.67 \\  & MNB & 88.46 & 88.86 & 88.46 & 88.33 \\  & BNB & 79.12 & 81.64 & 79.12 & 78.28 \\  & DT & 85.16 & 85.18 & 85.16 & 84.94 \\  & RF & 86.81 & 86.94 & 86.81 & 86.70 \\  & LR & 85.71 & 87.10 & 85.71 & 85.30 \\  & KNN & 67.03 & 76.93 & 67.03 & 61.74 \\ \hline \multirow{8}{*}{**KNN**} & SVM & 89.01 & 89.76 & 89.01 & 88.82 \\  & GNB & 68.68 & 80.59 & 68.68 & 70.80 \\  & MNB & 87.91 & 88.40 & 87.91 & 87.76 \\  & BNB & 76.37 & 79.69 & 76.37 & 75.17 \\  & DT & 83.52 & 83.59 & 83.52 & 83.3 \\  & RF & 87.36 & 87.73 & 87.36 & 87.22 \\  & LR & 84.62 & 86.31 & 84.62 & 84.14 \\  & KNN & 65.38 & 76.31 & 65.38 & 59.50 \\ \hline \multirow{8}{*}{**KNN**} & SVM & 86.26 & 87.27 & 86.26 & 85.98 \\  & GNB & 68.68 & 80.59 & 68.68 & 70.80 \\ \cline{1-1}  & MNB & 87.36 & 87.92 & 87.36 & 87.18 \\ \cline{1-1}  & BNB & 76.37 & 80.58 & 76.37 & 74.77 \\ \cline{1-1}  & DT & 84.07 & 84.03 & 84.07 & 83.92 \\ \cline{1-1}  & RF & 86.26 & 86.53 & 86.26 & 86.09 \\ \cline{1-1}  & LR & 82.97 & 85.17 & 82.97 & 82.37 \\ \cline{1-1}  & KNN & 64.84 & 78.99 & 64.84 & 58.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Performance of ML classifiers for N-gram feature. Table 14 illustrates the overall performance of the eight classifiers on the Arabic SA task when each feature from the emoji-based (second feature set) is used separately or combined. When using the feature F5 (the frequency of positive emojis), the SVM classifier achieves the best results. When the feature F6 (the frequency of negative emojis) is employed, the SVM and RF classifier have the highest accuracy (89.01%). The SVM classifier achieves the best performance when the feature F7 (the frequency of neutral emojis) is utilized. When all features are merged, SVM achieves the best performance. These findings led to the conclusion that the features in this feature set (based on emojis) are suitable for sentiment categorization when employed singly or in combination. This means that simply counting the number of emojis in a tweet might give you a reasonable idea of its overall sentiment. Table 15 displays the overall performance of the eight classifiers on the Arabic SA task when each feature from negation-based (the third feature set) is used separately or combined. When the feature F9 (the frequency of negation words) is utilized, the SVM classifier achieves the best performance. When the feature F10 (the presence/absence of negation word) is employed, the SVM classifier achieves the best performance. It is worthwhile to note that when all features are taken into account SVM provides the best results. From previous findings, it was inferred that the features in the negation-based feature set, whether used alone or combined, are suitable for the sentiment classification. This suggests that only presence/absence of negation word in a tweet is a reliable predictor of its overall sentiment.