# Developing Analytical Tools for Arabic Sentiment Analysis of COVID-19 Data
## 1 Introduction  
Many diseases are associated with the coronavirus family. They have the potential to induce symptoms extending from the common cold to Middle East respiratory syndrome and severe acute respiratory syndrome. COVID-19 is the most novel form of coronavirus that has never been identified in human beings before. Coronavirus are widely spread among mammals have been transferred from animals to humans. The World Health Organization (WHO) announced COVID-19 as a global pandemic where almost everyone in the world was under the high probability of exposure to COVID-19 in one way or another ([[https://www.who.int/emergencies/diseases/novel-coronavirus-2019](https://www.who.int/emergencies/diseases/novel-coronavirus-2019)]([https://www.who.int/emergencies/diseases/novel-coronavirus-2019](https://www.who.int/emergencies/diseases/novel-coronavirus-2019)), accessed on 2 December 2022). Social media plays a key role in transferring information about the novel coronavirus outbreaks. Twitter is considered one of the most powerful platforms in processing the COVID-19 information, with around 500 million tweets per day and 5787 tweets per second. Adults significantly increased Twitter usage during the pandemic ([[https://blog.hootsuite.com/twitter-statistics/](https://blog.hootsuite.com/twitter-statistics/)]([https://blog.hootsuite.com/twitter-statistics/](https://blog.hootsuite.com/twitter-statistics/)), accessed on 22 December 2022). People use Twitter to share their personal experiences and spread good wipes among those who are infected. SA is a method for classifying the opinions expressed at word, sentence, or even document level [1]. SA has gained significant attention from researchers in recent years, and significant progress was made for several languages, most notably English. However,as this study is confined to the Arabic language, SA is still a difficult undertaking. The difficulties encountered in Arabic SA are due to the language's inflectional structure [2], the prevalence of dialects, the absence of resources and tools for Arabic dialects, the restriction of Arabic sentiment lexicons, and the use of compound words and idioms, among other things. There are two basic methodologies reported in the literature for SA. A machine learning approach that employs annotated data as training data for building a model that can predict the classes of unseen data along with one of the ML classifiers (NB, SVM, Decision Tree (DT)). The second approach is semantic orientation, which employs sentiment lexicons and other linguistic resources to determine the sentiment of a particular phrase depend on the polarity of its terms [3]. SA has a lot of challenges, one of the most challenging to solve is negation in general terms. In SA, negation words can have an impact on the meaning of a statement, resulting in a shift in the sentiment orientation. For example, negation words (such as \"not\") and redirection emotion of phrases (such as \"good\"). in order to address this challenge, which is complex since it requires the recognition of negation words and a subsequent determination of the impacted terms, also known as negation scope, must be determined using semantic or syntactic representations [4]. Numerous studies have addressed the issue of recognizing negation phrases and the scope of negation to enhance the effectiveness of machine learning approach, such as those conducted by [5; 6; 7], among others. Since there are not many Arabic corpora and lexicons available to the public for SA [8; 9], especially for COVID-19 [10; 11; 12], the importance of this publication is in developing a lexicon and corpus for COVID-19 and making them available. An additional focus of this study is to describe how to construct an effective SA tool using a lexicon and to examine the relationship between lexicon-based rules, such as negation and emojis. Our sentiment analyzer also has the ability to deal with both negation and emojis, which are usually not used in current studies on Arabic SA, according to what we know so far.