# Developing Analytical Tools for Arabic Sentiment Analysis of COVID-19 Data
## 3 Research Methodology
### Lexicon Construction
#### 3.1.1 Constructing Keyword and Opinion Rules Lexicons  
The first phase in developing our Arabic lexicon for SA was to create two lexicons, a keyword lexicon and an opinion rules lexicon. To construct the keyword lexicon, we depend on two existing Arabic lexicons. The reasons behind the selection of these lexicons include their wide acceptance, the MSA coverage, as well as their relatively wide coverage. The first lexicon was NileULex, which had entries that were separated into compound phrases, single terms, or common idioms. We modified this lexicon to have only single words by deleting both common idioms and compound phrases. The second lexicon is the Arabic semi-lexicon, which has 2704 negative words and 1176 positive words written in MSA and annotated with their inflected forms, dialects synsets, POS, and polarity scores. Table 1 describes the statistics of the two used Arabic lexicons. We combine these two lexicons, then store all positive sentiment words in a separate file, and the same goes for all the words containing a negative sentiment. Finally, the keyword lexicon contained 2457 terms with a positive polarity and 6397 words with a negative polarity.  
To improve the efficiency of our lexicon, we have constructed an opinion rules lexicon, an opinion rule is an implication with implied opinion on the right and an expression on the left. The expression is conceptual since it reflects a notion that can be represented in a variety of ways in a sentence. For example, \"_\"_\"_\"_\"_\"_\"_\"_\"_\"_\"_\"_\"_\"_\"_\"_\"_\"_  
negation terms used in MSA and Egyptian dialect, and then store them in a negation file which includes 34 Arabic words such as \"_\"_\"_\"_\"_\"_\"_\"_.  
On the other hand, Emojis are visual symbols, also known as ideograms, that represent not only face reactions but also ideas and concepts including vehicles, celebration, weather and buildings, foods and drink, animals and plants, or feelings, emotions, and activities. We measured the sentiments of emojis using negative, positive, and neutral values rather than a Likert scale. This is due to the difficulties of achieving intercoder reliability using a scalar technique, made much more challenging by the fact that the emojis we studied were uniquely associated with the COVID-19 pandemic.  
We used the emojipedia ([[https://emojipedia.org](https://emojipedia.org)]([https://emojipedia.org](https://emojipedia.org)), accessed on 4 January 2023) to classify emoji into its corresponding sentiments. The emojpedia is a centralized database of the majority of emojis available on modern smart devices, and it contains information about each emoji's numerous meanings, interpretations, Unicode, and usage. At first, as shown in Table 2, appropriate positive emojis were used to represent positive sentiment emotions. Smiles, laughter, love, and hugs all convey positive moods through facial expressions. Similarly, neutral sentiment emotion has been described in order to maintain appropriate neutral emojis. We have already looked at straight face, no expression, shock, flags, foods, buildings, animals, surprise, and indecision as examples of neutral facial expressions that can be analyzed. Negative sentiment emotion has been described to maintain relevant negative emojis. We have begun to study anger, shame, sadness, worry, disgust, and crying. Each positive emoji character was stored in a positive emoji list totalling 69 emojis. A neutral emoji character was saved in the neutral emoji list, totalling 199 emojis, while the negative emoji list included the emoji characters that have negative sentiment totalled 52.