# 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
Each of the SVM, RF, and LR models have the first rank with a greater accuracy of (92.21%). While the DT come in the second rank with accuracy of (90.26%). The third rank was associated with the KNN with accuracy of 88.31%. The fourth rank was Bernoulli NB with accuracy of 86.36%. The fifth rank come with Multinomial NB with accuracy of 85.71%. The last rank come with Gaussian NB with accuracy of 80.52%. As seen in Figure 5, the use of all features considerably increased the accuracy of sentiment classification throughout all assessed classifiers. SVM achieves the highest accuracy (92.86%) when employing sentiment-based features, followed by LR (90.91%). When applying emoji-based features, the SVM achieves the highest accuracy (89.61%), followed by RF (88.96%) and LR (88.96%). The use of emoji-based characteristics dramatically enhanced the accuracy of sentiment classification on all classifiers investigated. When applying negation-based features, SVM achieves the maximum accuracy (90.26%), followed by RF (88.31%). As the best-performing classification classifier, SVM improved sentiment classification accuracy by roughly 3.85% when using the sentiment-based features, 0.6% when using the emoji-based features, 1.25% when using the negation-based features, and 3.2% when combining all features. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature** & **Classifier** & **Accuracy** & **Precision** & **Recall** & **F-Score** \\ \hline  & SVM & 92.21 & 92.32 & 92.21 & 92.23 \\  & GNB & 80.52 & 81.25 & 80.52 & 80.28 \\  & MNB & 85.71 & 86.91 & 85.71 & 85.78 \\ \(\frac{\text{cl}}{\text{cl}}\) & BNB & 86.36 & 86.34 & 86.36 & 86.27 \\  & DT & 90.26 & 90.34 & 90.26 & 90.25 \\  & RF & 92.21 & 92.32 & 92.21 & 92.23 \\  & LR & 92.21 & 92.24 & 92.21 & 92.22 \\  & KNN & 88.31 & 88.40 & 88.31 & 88.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Performance of ML classifiers for all feature sets. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature** & **Classifier** & **Accuracy** & **Precision** & **Recall** & **F-Score** \\ \hline  & SVM & 90.26 & 90.80 & 90.26 & 90.31 \\  & GNB & 79.87 & 80.84 & 79.87 & 79.68 \\  & MNB & 85.71 & 86.16 & 85.71 & 85.80 \\ \(\frac{\text{cl}}{\text{cl}}\) & BNB & 85.71 & 85.66 & 85.71 & 85.65 \\  & DT & 84.42 & 84.38 & 84.42 & 84.39 \\  & RF & 88.31 & 88.30 & 88.31 & 88.29 \\  & 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: _Cont._

## 5 Conclusions and Future Work

In this paper, we built ArSentiCOVID a large-scale Arabic sentiment lexicon about COVID-19 that took advanced linguistic phenomena like negation and emojis into account. The lexicon is developed both manually and automatically. The automatic step is articulated using two main methods, using the pre-created two lexicon and extract words from annotated dataset about COVID-19. For the manual step, we extracted and hand-labeled three extensive word lists, negation and emoji and word list from SenWave dataset. In addition, we developed a sentiment analyzer tool based on a lexicon that is capable of appropriately handling negation and emoji. As a result, we created a large-scale Arabic annotated sentiment corpus about COVID-19 collected from Twitter and contain more than 42000 tweets. To test the efficiency of the developed lexicon we run two experiments, using the AraCOVID19-SSD dataset. For the first experiment we test the effect of applying negation and emoji rules or both in the created lexicon by employing a lexicon-based approach to conduct a three-way classification on the dataset. The accuracy of the lexicon is improved by 2.13% when emoji rules are used, 4.13% when negation rules are applied, and 6.13% when both are applied. For the second experiment, we applied an ensemble method that uses lexicon-based sentiment polarity, negation, and emojis as input features for the ML approach.