# Developing Analytical Tools for Arabic Sentiment Analysis of COVID-19 Data
## 2 Related Works
### Arabic Lexicon Construction  
Lexicons in SA include a group of words with their related polarity rather than a word and its definition like in dictionaries. The sentiment lexicon is considered the most crucial resource for most SA algorithms. There are three approaches to the construction of the sentiment lexicon, manual, automatic, and semi-automatic. Manual lexicon construction requires human effort to extract sentiment words from Arabic dictionaries or from Arabic sentiment analysis datasets, and then label these words with their polarity. [PERSON] et al. [13] concentrated on the Algerian dialect and the creation of a lexicon with a manual translation of the current MSA and Egyptian lexicon. The lexicon is divided into three sections, keywords, negation words, and intensification words. The authors subsequently expanded their work with two lexicons, one for emoticon vocabulary and the other for popular words. The Keyword Lexicon consisted of 2380 negative polarity words and 713 positive polarity words. The intensification words lexicon was built from MSA and then added the equivalent from the Algerian dialect. Experimental results demonstrate that their method performed well with an accuracy of 79.13%. The lexicon cannot handle emojis. Another work done by [14] developed a lexicon for sentiment analysis of Saudi dialect tweets called SauDiSenti, which consists of 4431 words and phrases that were manually identified by two Saudi annotators from previously annotated datasets of Saudi dialect tweets. They assessed SauDiSenti's performance using a new dataset labeled by two annotators, consisting of 1500 tweets uniformly dispersed across three classes, positive, neutral, and negative. The lexicon cannot handle negation or emojis. [PERSON] et al. [15] provided ArSEL, the first large scale Arabic sentiment and emotion dictionary. ArSEL is automatically generated by combining three lexical resources, DepecheMood, EWN, and ArSenL. At first, DepecheMood is mapped to EWN. Then it is iteratively enlarged utilizing the EWN synonymy semantic relation. Then the resulting extended DepecheMood version, EmoWordNet, is linked to ArSenL items using EWN synset IDs in both lexicons. ArSEL is made up of 32196 Arabic lemmas labelled concurrently with sentiment and emotion scores. The lexicon was evaluated using the SemEval 2007 Affective Task Arabic annotated dataset and the Arabic dataset from SemEval 2018 Task1. Coverages of 91% and 84% are obtained on both datasets, respectively. On the SemEval 2018 Arabic dataset, the average F1 metric for emotion classification improved by 30% when compared to the majority baseline. Another work carried out by [PERSON] et al. [16], where the lexicon is generated automatically by using the English dictionary \"SOCAL\". SOCAL has 6769 words with sentiment labels ranging from (1, 5) for negative words to (+1, +5) for positive words. Words were translated into Arabic, while their polarity remained unchanged. The dictionary has 4873 annotated terms ranging from (\(-\)5 to +5), with 2483 in Arabizi and 2390 in Arabic. The lexicon that was created was subsequently utilized to label an Algerian dataset. The suggested method generates automatically sentiment dataset of 8000 messages of which 4000 are allocated to Arabizi and 4000 to Arabic into positive and negative. The obtained F1-score is up to 72% for an Arabic dataset, while, for an Arabizi dataset, it is up to 78%. All previous lexicons could not handle negation. Semi-automatic lexicon construction, the lexicon is built automatically and then manually reviewed. [PERSON] et al. [3] presented the Arabic senti-lexicon, a collection of 3880 positive and negative synsets labelled with their POS, dialect synsets, polarity scores, and inflected forms. The authors also developed a Multi-domain Arabic Sentiment Corpus (MASC) which contains 8860 positive and negative reviews from 15 domains. Two native speakers were instructed to identify these reviews with favorable and negative ratings written in various dialects in Arabic.