# 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
[CrossRef]
* (15) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] [PERSON]: A large scale arabic sentiment and emotion lexicon. _OSACT_**2018**, \(3\), 26. * (16) [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Sentialg: Automated corpus annotation for algerian sentiment analysis. In Proceedings of the International Conference on Brain Inspired Cognitive Systems, Xi'an, China, 7-8 July 2018; Springer: Berlin/Heidelberg, Germany, 2018; pp. 557-567. * (17) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] A large scale Arabic sentiment lexicon for Arabic opinion mining. In Proceedings of the EMNLP 2014 workshop on arabic natural language processing (ANLP), Doha, Qatar, 25-29 October 2014; pp. 165-173. * (18) [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; et al. Fighting the COVID-19 infodemic: Modeling the perspective of journalists, fact-checkers, social media platforms, policy makers, and the society. _arXiv_**2020**, arXiv:2005.00033. * (19) [PERSON]; [PERSON] COVID-19 and Arabic Twitter: How can Arab world governments and public health organizations learn from social media? In Proceedings of the 1 st Workshop on NLP for COVID-19 at ACL 2020, Online, 9 July 2020. * (20) [PERSON]; [PERSON] [PERSON]: Analyzing arabic tweets in the early days of coronavirus (COVID-19) pandemic. _arXiv_**2020**, arXiv:2012.01462. * (21) [PERSON] [PERSON] Nileulex: A phrase and word level sentiment lexicon for egyptian and modern standard arabic. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), Portoroz, Slovenia, 23-28 May 2016; pp. 2900-2905. * (22) [PERSON]; [PERSON]; [PERSON]; [PERSON] Arabic sentiment analysis: Lexicon-based and corpus-based. In Proceedings of the 2013 IEEE Jordan conference on applied electrical engineering and computing technologies (AEECT), Amman, Jordan, 3-5 December 2013; pp. 1-6. * (23) [PERSON]; [PERSON]; [PERSON]; [PERSON] Twitter sentiment analysis: Lexicon method, machine learning method and their combination. _arXiv_**2015**, arXiv:1507.00955. * (24) [PERSON]; [PERSON] Valence Shifter Rules for Arabic Sentiment Analysis. _Int. J. Multidiscip. Sci. Adv. Technol._**2020**, \(1\), 167-184. * (25) [PERSON]; [PERSON] [PERSON] AraCOVID19-SSD: Arabic COVID-19 Sentiment and Sarcasm Detection Dataset. _arXiv_**2021**, arXiv:2110.01948. * [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] Sentiment analysis of Arabic tweets regarding distance learning in Saudi Arabia during the COVID-19 pandemic. _Sensors_**2021**, _21_, 5431. [CrossRef] [PubMed]
* [PERSON] (1998) [PERSON] Text categorization with support vector machines: Learning with many relevant features. In Proceedings of the European Conference on Machine Learning, Chemnitz, Germany, 21-23 April 1998; Springer: Berlin/Heidelberg, Germany, 1998; pp. 137-142. * [PERSON] et al. (2003) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] KNN model-based approach in classification. In _On the Move to Meaningful Internet Systems, Proceedings of the OTM Confedrated International Conferences_; Springer: Berlin/Heidelberg, Germany, 2003; pp. 986-996. * [PERSON] and [PERSON] (1977) [PERSON]; [PERSON] The decision tree classifier: Design and potential. _IEEE Trans. Geosci. Electron._**1977**, _15_, 142-147. [CrossRef]
* [PERSON] (2001) [PERSON] Random forests. _Mach. Learn._**2001**, _45_, 5-32. [CrossRef]
* [PERSON] and [PERSON] (1990) [PERSON]; [PERSON] Multiple decision trees. In _Machine Intelligence and Pattern Recognition_; Elsevier: Amsterdam, The Netherlands, 1990; Volume 9, pp. 327-335. * [PERSON] (2000) [PERSON] An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. _Mach. Learn._**2000**, _40_, 139-157. [CrossRef]
* [PERSON] and [PERSON] (1989) [PERSON]; [PERSON]; [PERSON] Best subsets logistic regression.