# 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
The results of the second experiment reveal that using sentiment-based features, emoji-based features, negation-based features, and all features with the SVM classifier improved opinion classification accuracy by about 3.85%, 0.6%, 1.25%, and 3.2%, respectively. Future work will include assigning sentiment scores to the lexicon entries. Extending the lexicon and experimenting with different types of datasets. Scraping more data about COVID-19 from different social networking websites. Both the lexicon and corpus are available upon request. Methodology, N.A. and T.H.A.S. ; software, N.A. ; writing--original draft, N.A. ; writing--review and editing, I.E.E., M.F.F. and T.H.A.S. ; supervision, I.E.E., M.F.F. and T.H.A.S. ; formal analysis, N.A., I.E.E., M.F.F. and T.H.A.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. **Data Availability Statement:** Data will be made available on reasonable request. **Conflicts of Interest:** The authors declare no conflict of interest. Figure 5: Accuracy measure for ML classifiers using different features. ## References

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