## 9 Conclusions and Future Scope  
In this work, we studied the potential of social media such as Twitter as a platform to depict the emotions and thereby the mental wellbeing of its users. In particular, we studied the contrasting emotions of people residing in a holy city and a typical metropolitan city. To mine emotions from user tweets, we designed a bilingual lexicon (Arabic and English) called AELex based on two recently proposed emotion lexicons ([PERSON]'s Arabic Lexicon and the Moodbook English lexicon). The proposed study was an attempt to show that user tweets have the potential to unveil user emotions and thus can help in analyzing user emotions as well as behavior on different platforms. Additionally, emotions are strongly influenced by the surrounding environment. Therefore, in this study, we performed a comparative analysis of tweets posted by users in Riyadh and Mecca (pilgrimage city). [PERSON]'s eight emotion categories of _Fear_, _Anger_, _Sadness_, _Joy_, _Surprise_, _Disgust_, _Trust_, and _Anticipation_ were used for this purpose. To be more vigilant toward the outcomes, the entire analysis was performed on two datasets containing Arabic and English language tweets separately.  
The study clearly showed the impact of social and habitual surroundings on user tweets and thereby their emotions. People residing in pilgrimage cities were observed to be more vocal in expressing positive emotions or sentiments. They also conveyed less negativity in comparison to people residing in a non-pilgrimage city. Furthermore, a higher proportion of people residing in a holy city or pilgrimage area expressed emotions in  
Figure 5: Word-cloud of Mecca users.  
Figure 6: Word-cloud of Riyadh users.  
positive categories such as _Joy_ and _Anticipation_, whereas negative emotions were expressed more often by people residing in a typical metropolitan city.  
This study could be extended to detect early signs of a psychological disorder such as depression or suicidal tendencies among residents of such contrasting social environments. This social media text-based analysis could be further improved if we also take into account the context of the tweets and also their sarcasm, if present. Furthermore, this analysis could assist in building a machine learning-based model to predict the location (city, state, etc.) of a tweet/post.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] created the bilingual lexicon., [PERSON] and [PERSON] analyzed the data; [PERSON] prepared the first draft of the paper with [PERSON] as Arabic language expert; [PERSON] and [PERSON] edited the paper; [PERSON] proofread the paper and supervised the overall work. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Deanship of Scientific Research at Princess Nourah hint Abdulrahman University through the Program of Research Project Funding after Publication. Grant No. (41-PRFA-P-19).  
The study was conducted according to the guidelines of the Institutional Review Board (IRB) at Princess Nourah bin Abdulrahman University, Riyadh, Saudi Arabia.  
Not applicable.  
The link to the data repository related to this project can be made available upon request. If required will be made publicly available on GitHub profile ([[https://bit.ly/3](https://bit.ly/3) jku4 En]([https://bit.ly/3](https://bit.ly/3) jku4 En), accessed on 23 July 2021) under the title of the paper.  
The authors declare no conflict of interest.