## 7 Data Analysis
### Phase 1: Sentiment Polarity Analysis (Positivity and Negativity)  
The objective of this phase was to determine if the user tweets revealed the peace and positivity experienced by residents living in a holy place. Therefore, we compared the amount of positivity and negativity in user tweets. AEELex contains a list of positive and negative emotion words. _Joy, Trust_, and _Anticipation_ contain words belonging to the positive category whereas _Fear, Sad, Angry_, and _Disgust_ comprise words falling under the negative category. Words belonging to the _Surprise_ category were classified based on the context. For each region, we calculated the positivity and negativity among the users with Equations (1) and (2), respectively. \[pos_{regian}=\frac{Positive EmotionWords(region)}{Total EmotionWords(region)} \tag{1}\]  
\[neg_{regian}=\frac{Negative EmotionWords(region)}{Total EmotionWords(region)} \tag{2}\]  
where, \(pos_{region}\) and \(neg_{region}\) are the positivity and negativity of a region, respectively. \(Positive EmotionWords(region)\), \(Negative EmotionWords(region)\) are the number of positive \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Lexicon**} & \multirow{2}{*}{**\# Emotion Categories**} & **Distinct** & \multirow{2}{*}{**Positive/** Negative** Polarity**} & **\# Tweets Where** \\  & & **Emotion** & & **Lexicon was** \\  & & **Categories** & & **Not Able to** \\  & & **Identified** & & **Find Any** \\ \hline [PERSON] & 6 (Fear, Anger, Sadness, & & & \\ lexicon [35] & Joy, Surprise, and Disgust) & 6 & No & 595 \\ AEELex (Arabic) & 8 ([PERSON] lexicon + Trust and Anticipation) & 8 & Yes & 177 \\ Moodbook & 8 (Fear, Anger, Sadness, & & & \\ Joy, Surprise, and Disgust, & & & & \\ Trust and Anticipation) & 8 & Yes & 732 \\ AEELex (English) & 8 (Fear, Anger, Sadness, & & & \\ Joy, Surprise, and Disgust, & & & & \\ Trust and Anticipation) & 8 & Yes & 663 \\ \hline \hline \end{tabular} \# means ’number of’.  
\end{table} 
Table 6: Comparison of AEELex with other lexicons. and negative emotion words of a region, respectively, under eight emotion categories and \(TotalEmotionWords(region)\) is the total number of emotion words extracted from the tweets of a user belonging to that region. Furthermore, the plots in Figure 2 show the proportion of positive and negative feelings expressed by people residing in the cities of Mecca and Riyadh. It is observed from the plots that users from both regions expressed positive emotions much more than negative ones. However, upon conducting the experiments (to support our comparative study), we observed that users from Mecca city expressed a high degree of positivity (-90%) in comparison to the positivity expressed by users residing in Riyadh (-70%). This can be attributed to the fact that people residing in holy places are regularly exposed to religious remembrance and rituals that inculcate a shared feeling of inner harmony due to spiritual enlightenment and inner satisfaction. Conversely, people residing in a metropolitan city such as Riyadh live a fast, competitive, stressful, and busy life. The positivity of Mecca residents could be seen from their tweets about [PERSON] and friendship and positivity such as \" _\" _\" _\" _\" _\" _\" _\" _\" _\" _\" _\" _\" _\" _\" _\" _\" _\" _\" _\" _\" _\" _\" _\" _\"_ _\" _\"_ _\" _\"_ _\" _\"_ _\"_\" _\" _\"_ _\"_ _\"_ _\"_ _\" _\"_\"  
proportion of the users in Mecca, approximately 20%, expressed the emotion _Joy_. In this city, the users use words such as \"_\"_\" (flower), \"_\"_\" (love), etc. in their tweets: for example, \"_\"_\" (love), \"_\"_\" (love), etc. in their tweets: for example, \"_\"_\" (love), \"_\"_\" (l just imagined that it was blue roses) expressing positivity and happiness, \"_\"_\" _\"_\" _\"_\" _\"_\" _\"_\" _\"_\" _\"_\" (l appreciate deep, beautiful friendships full of love), etc. For emotions falling in the negative categories, _Sadness_ (_\"_\"_) was the one most expressed. Users from both regions used words such as \"_\" (tired), \"_\"_\" (pain), etc.