Analyzing User Digital Emotions from a Holy Versus Non-Pilgrimage City in Saudi Arabia on Twitter Platform

applied sciences

Article Analyzing User Digital Emotions from a Holy versus Non-Pilgrimage City in Saudi Arabia on Twitter Platform

Kashish Ara Shakil 1, Kahkashan Tabassum 1, Fawziah S. Alqahtani 1 and Mudasir Ahmad Wani 2,*

1 Department of Computer Science, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh Saudi 11671, Saudi Arabia; [email protected] (K.A.S.); [email protected] (K.T.); [email protected] (F.S.A.) 2 Department of Information Security and Communication Technology, Norwegian University of Science and Technology (NTNU), 2815 Gjøvik, Norway * Correspondence: [email protected]

Abstract: Humans are the product of what society and their environment conditions them into being. People living in metropolitan cities have a very fast-paced life and are constantly exposed to different situations. A social media platform enables individuals to express their emotions and sentiments and thus acts as a reservoir for the digital emotion footprints of its users. This study proposes that the user data available on Twitter has the potential to showcase the contrasting emotions of people residing in a pilgrimage city versus those residing in other, non-pilgrimage areas. We collected the Arabic geolocated tweets of users living in Mecca (holy city) and Riyadh (non-pilgrimage city). The user emotions were classified on the basis of Plutchik’s eight basic emotion categories, Fear, Anger, Sadness, Joy, Surprise, Disgust, Trust, and Anticipation. A new bilingual dictionary, AEELex (Arabic English Emotion Lexicon), was designed to determine emotions derived from user tweets. AEELex has been validated on commonly known and popular lexicons. An emotion analysis revealed that people Citation: Shakil, K.A.; Tabassum, K.; living in Mecca had more positivity than those residing in Riyadh. Anticipation was the emotion Alqahtani, F.S.; Wani, M.A. Analyzing User Digital Emotions from a Holy that was dominant or most expressed in both places. However, a larger proportion of users living in versus Non-Pilgrimage City in Saudi Mecca fell under this category. The proposed analysis was an initial attempt toward studying the Arabia on Twitter Platform. Appl. Sci. emotional and behavioral differences between users living in different cities of Saudi Arabia. This 2021, 11, 6846. https://doi.org/ study has several other important applications. First, the emotion-based study could contribute to 10.3390/app11156846 the development of a machine learning-based model for predicting depression in netizens. Second, behavioral appearances mined from the text could benefit efforts to identify the regional location of a Academic Editors: Paolo Bottoni and particular user. Emanuele Panizzi Keywords: emotion analysis; Twitter; online social network analysis; behavioral analysis Received: 25 June 2021 Accepted: 23 July 2021 Published: 25 July 2021 1. Introduction Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in Emotions are a vital source of information to assess the overall psychological well- published maps and institutional affil- being and health of individuals. Emotion plays an important role in the overall behavior iations. and reactions of an individual in different circumstances. Findings have shown that an individual’s response to natural surroundings has an impact on their parasympathetic nervous system. The surroundings also have a positive impact on a person’s emotional state and psychological activity level [1]. Thus, people living in varying regions will have a different emotional state. The traditional methods for studying these individual states Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. involved questionnaires, interviews, or in-person observations. However, such studies to This article is an open access article date generally have been marked by small sample sizes and involved labor-intensive efforts distributed under the terms and in reaching out to individuals, leading to inconsistency and inaccuracy in the inferences conditions of the Creative Commons obtained. In the present-day world, social media sites such as Twitter, LinkedIn, and Attribution (CC BY) license (https:// Facebook are increasingly becoming platforms for individuals to express their emotions, creativecommons.org/licenses/by/ opinions, and ideas. These days people are more expressive on such platforms, as they 4.0/). can fully express themselves without revealing their real identities or endure the stigma of

Appl. Sci. 2021, 11, 6846. https://doi.org/10.3390/app11156846 https://www.mdpi.com/journal/applsci Appl. Sci. 2021, 11, 6846 2 of 15

being judged. Thus, digital footprints allow social media platforms to serve as a rich source of information to investigate the emotional dimensions, sentiments, and psychological behavior of individuals. These data, accumulated over time, have led to granular emotion studies for every user. For example in one of the studies, tweets were used to analyze anti-LGBT campaigns in Indonesia [2]. In another work, Twitter messages were used to analyze dialogue during the Austrian presidential elections in 2016, concluding that the winning candidate sent tweets that were neutral in comparison to his opponent, whose messages were based on emotions [3]. Saudi Arabia is a country that is transforming itself into a social media powerhouse. Of its total population of 34.54 million, 25 million are active social media users, and of these, 20.03 million are Twitter users [4]. With a majority of the country’s population being avid Twitter users, we can use the data they share to understand how user environments affect their overall mental wellbeing and behavior. To gauge the emotional atmospheres of residents, we studied the tweets shared by users in Saudi Arabia hailing from different backgrounds. In particular, we studied the tweets of users from a holy city or place of pilgrimage i.e., Mecca, and a non-pilgrimage metropolitan city i.e., Riyadh. This work sought to show the potential of social media data shared by users in uncovering the impact of users’ surroundings on their mental health. Another goal was to show how their emotions vary across different social environments. At present most of the related work on identifying emotions on social media has focused on assessing user sentiments. Approaches such as supervised deep learning, semi-supervised methods, natural language processing, and lexicon-based analysis have been used to detect user sentiment polarity of social media data [5–7]. Due to the increased interest of users in performing analysis of social media conversations, a new field of emotion analysis has emerged [8]. In the current literature, studies have been carried out to understand and detect different forms of emotions such as sarcasm [9], rumors [10], depression [11–13], and suicidal tendencies [14]. However, there are primary limitations on literature research in this field. Given the thrust of analyzing user emotions, there is a need to focus on understanding the basic emotion categories, which act as a foundation toward effectively understating emotional behaviors. Deriving the emotions of users rather than their sentiment polarities (positive and negative) will enable us to investigate the digital emotion footprint of users. Therefore, to gain deeper insight into specific emotion dimensions and categories, we focused on the study of basic emotion categories proposed by [15]: namely, fear, trust, surprise anger, sadness, joy, disgust, and anticipation. We designed a bilingual (English and Arabic) emotion lexicon named AEELex based on the Moodbook [16] and NRC Word-Emotion Lexicon (Emolex) [17]. We have extended these dictionaries for the Arabic language, along with new words from user tweets, to form our bilingual dictionary. Following are the main contributions of our work: • The geolocated tweets from two cities in Saudi Arabia have been extracted using Twitter API (the dataset will be made available with the algorithms for the researchers working in this domain). • AEELex, a bilingual emotion lexicon, has been designed to extract the different emotions of users in two different (pilgrimage and non- pilgrimage) cities. • The potential of user tweets to harness user emotions has been studied. • The different emotion-expressing behaviors of netizens across two different cities has been analyzed and compared. • The most common emotion-based vocabulary terms (both Arabic as well as English) belonging to eight emotion categories from both user groups have been identified. The rest of this paper is organized as follows. Section2 presents related work. Section3 describes the approach we adopted for our study. Sections4–6 describe the data collection, lexicon design, and lexicon comparison and validation respectively. Section7 presents a data analysis. Section8 is a discussion on the inferences from the study. Section9 contains the conclusions of the paper, outlining potential research directions and the contributions of this study. Appl. Sci. 2021, 11, 6846 3 of 15

2. Related Work Social media data have been used in many applications across different domains for various purposes, including detecting humor in social media slang used on platforms such as Weibo, the largest Chinese social network [18], or analyzing the spatial patterns of emotions expressed by users visiting theme parks such as Disney World within the dimensions of high and low arousal or pleasure and displeasure [19]. Several studies have been performed to identify user satisfaction based on positive emotions such as happiness and pleasure [20]. Thus, user emotions vary with changes in their region and are also reflected in their social media accounts. One such study showed how the emotions and sentiments of users toward climate change vary between different countries such as the U.K. and Spain. It showed that Twitter users from the U.K. talked less negatively about the weather in comparison to Spanish citizens and had anticipation as their predominant emotion [21]. In [16] users0 emotions were analyzed in a region in conflict, Kashmir, and a region marked by the absence of conflict, Delhi, two union territories in India. The authors showed that people residing in to conflict-impacted regions have a negative attitude and express negative emotions such as anger, fear, and sadness in their Facebook posts. The proposed study also analyzed users’ emotions across different regions in two different cities in Saudi Arabia. Furthermore, to bridge the gap between different nationals and tourists residing in a given region, and for more accurate analytics, we used data that were in the native language of the country i.e., Arabic. Most of the work performed with tweets from Saudi Arabia has focused on sentiment and semantic analysis [22,23]. Words were labeled based on their polarity. In [24] SANA, a multi-dialect, multi-genre, and multilingual lexicon, was created based on text from the Egyptian chat room on Yahoo Maktoob. The words here were labeled based on their positive, negative, and neutral content. In [25], a study was performed on social media data collected from YouTube, Facebook, Al-Saha Al-Siyasia, and Al Arabiya based on the 2009 and 2011 floods in Jeddah. The study showed that people on YouTube posted more emotional comments than those on Facebook. The users expressed emotions such as sadness for the loss of lives and anger toward authorities. Saudi Mood [25] is a real-time tool for visualizing the emotions of users on Twitter, covering happy, sad, angry, scared, and surprised emotions only. The authors in [26] manually created a dataset of Arabic emotions using tweets, but the tweets were filtered from Egypt geolocation only. The emotions in this case were placed into the categories of happiness, joy, sadness, anger, disgust, fear, and surprise. Additionally, the data were categorized on the basis of only 5879 tweets. It has been observed that there are very few studies based on the Plutchik [15] emotion categories in the Arabic language. Most of the work in this discipline has been conducted using either sentiment or emotion analysis. However, the studies on emotion analysis have been limited either by their size or the emotion categories they assessed. This study is a maiden attempt to analyze the difference in emotions of users residing in a pilgrimage area versus those in a non-holy city. An analysis of this kind can assist in designing a machine learning-based system to predict the mental state of a user and the location of a tweet/post.

3. Approach Synopsis Our analysis involved four important steps, as shown in Figure1. In particular, we performed an analysis of the emotional behavior of people belonging to two different regions. The emotional behavior in this context could be deﬁned as the choice of emotion words used by individuals to express their feelings and opinions. Appl. Sci. 2021, 11, x FOR PEER REVIEW 4 of 16

4. Data Collection Data were collected through filtered real-time streaming API [27] provided by Twit- ter. User tweets were collected based on their locations and language, excluding retweets. The data were harvested from selected Twitter profiles for a span of 1.5 months, with an average of 20 tweets collected per user. The collected data did not take into account the activeness of the users. The tweet language was Arabic. In particular, we focused on Saudi dialects including Najdi Arabic, Gulf Arabic, etc. The users’ data were collected from two metro cities of the Kingdom of Saudi Arabia, i.e., Riyadh and Mecca. The regions were selected using the bounding box option that allows geo-tagged user tweets to be collected based on a user-defined bounding box area. The geolocation co-ordinates of Riyadh (45.04, 24.01, 47.91, 26.33) and Mecca (38.66, 18.1, 43.67, 23.97) were obtained using Klokantech bounding box tool [28]. The choice of these cities helped us in studying the contrasting emotions of people belonging to a place of pilgrimage, and a city within the Appl. Sci. 2021, 11, 6846 same country but not a place of pilgrimage. Table 1 shows the statistics of the4 of 15data collected.

FigureFigure 1. Approach synopsis synopsis for for studying studying holy holy city city versus versus non-holy non- city.holy city.

Table4. Data 1. CollectionStatistics of the data before preprocessing. Data were collected through filtered real-time streaming API [27] provided by Twitter. Item Size User tweets were collected based on their locations and language, excluding retweets. The data were harvestedTotal number from of selected users Twitter profiles for a span of 1.5 months,85,027 with an averageTotal ofnumber 20 tweets of t collectedweets (~20 per tweets user. The per collected user) data did not take1,700,540 into account the activeness ofTweets the users. collected The tweet from language Riyadh was Arabic. In particular, we focused951,600on Saudi dialects includingTweets Najdi collected Arabic, from Gulf Mecca Arabic, etc. The users’ data were collected748,940 from two metro cities of the Kingdom of Saudi Arabia, i.e., Riyadh and Mecca. The regions were Number of distinct users in Riyadh 47,580 selected using the bounding box option that allows geo-tagged user tweets to be collected based onNumber a user-defined of distinct bounding users box in area. Mecca The geolocation co-ordinates of37,447 Riyadh (45.04, 24.01, 47.91, 26.33) and Mecca (38.66, 18.1, 43.67, 23.97) were obtained using Klokantech 5.bounding Arabic English box tool Emotion [28]. The choiceLexicon of these(AEELex) cities Design helped us in studying the contrasting emotionsAEELex of people is a bilingual belonging emotion to a place lexicon of pilgrimage, for extracting and a city user within emotions. the same This country lexicon has beenbut not developed a place of in pilgrimage. Arabic and Table English1 shows language. the statistics This of section the data discusses collected. the steps for data preparationTable 1. Statistics and of design the data of before the preprocessing.proposed lexicon.

Item Size Total number of users 85,027 Total number of tweets (~20 tweets per user) 1,700,540 Tweets collected from Riyadh 951,600 Tweets collected from Mecca 748,940 Number of distinct users in Riyadh 47,580 Number of distinct users in Mecca 37,447

5. Arabic English Emotion Lexicon (AEELex) Design AEELex is a bilingual emotion lexicon for extracting user emotions. This lexicon has been developed in Arabic and English language. This section discusses the steps for data preparation and design of the proposed lexicon. Appl.Appl.Appl.Appl.Appl. Sci. Sci. Sci. Sci. Sci.20 20 2021 2021 2120, 21,11 ,2111 11,, , 11, x ,11x xFOR, FOR ,x,FOR x xFOR FORFOR PEER PEER PEER PEER PEERPEER REVIEW REVIEW REVIEW REVIEW REVIEWREVIEW 55 5of of 5of 5 16 of16 of16 16 16

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The schema for usersbutbut hadbutbut the the postedthe theirtheir respective irrespectiveirir respective respectiverespective more than tweets tweets tweets tweetstweets one were were tweet. were werewere merged merged merged Therefore, mergedmerged and and and andand preserved preserved only preserved preservedpreserved unique for for for usersforforanalysis. analysis. analysis. analysis.analysis. were The The retainedThe TheThe schema schema schema schemaschema for for for forfor the final data that were used for the experiments is shown in Table 3. but theirthethethethe thefinal final respective final finalfinal data data data datadata that that tweets that thatthat were were were werewere were used used used used merged for for for forthe the the the andexperiments experiments experiments experiments preserved is is forshown is shownis shown analysis.shown in in inT inTable ableT The Tableable 3. 3. schema 3. 3. for the ﬁnal data that were used for the experiments is shown in Table3. and", and ,, and and and م"مand , ",م ","م "م "م" " TableTableTableTableTable 2. 2. 2. 2.Information Information 2.Information Information InformationInformation on on on on ton on the the he t he t t mainhe main main main main Arabic Arabic Arabic Arabic Arabic diacritics diacritics diacritics diacritics diacritics (diacritic (diacritic (diacritic (diacritic (diacritic name, name, name, name, name, shape shape shape shape shape on on on on the on the the the the letter letter letter letter letter sound).sound).sound).sound).sound).sound). Table 2. Information on the main Arabic diacritics (diacritic name, shape on the letter "Ð", and sound). NameNameNameNameName ShapeShapeShapeShapeShape SoundSoundSoundSoundSound Name Shape Sound aa a a a م م م م م FathaFathaFathaFathaFatha Fatha a uuu u u م م م م م DammaDammaDammaDammaDamma Ð Damma Ð u ii i i ii م م م م م KasraKasraKasraKasraKasra Kasra Ð i anananan an م م م م م TanwinTanwinTanwinTanwinTanwin Fath Fath Fath Fath Fath Tanwin Fath Ð an inin in م م TanwinTanwinTanwin Damm Damm Damm in inin inin م م م م TanwinTanwinTanwin DammTanwin Damm Damm Damm Ð un unununun un م م م م م TanwinTanwinTanwinTanwinTanwin kasrTanwin kasr kasr kasr kasr kasr Ð No soundNoNoNoNo Nosound sound sound sound sound م م م م م SukunSukunSukunSukunSukunSukun Ð DoublingDoublingDoublingDoublingDoublingDoubling or extra or or or or length extraorextra extra extra extra length length length length length م م م م م ShaddaShaddaShaddaShaddaShaddaShadda Ð Tatwil/Kashida No phonic value NoNoNoNo Nophonic phonic phonic phonic phonic value value value value value ــ ـ ـ ــ Tatwil/KashidaTatwil/KashidaTatwil/KashidaTatwil/KashidaTatwil/Kashida

TableTableTableTableTable 44 4 shows4shows shows4 shows shows statistics statisticsstatistics statistics statistics on onon on on the thethe the the the data datadata data datadata afterafter after after afterafter data datadata data datadata preprocessing. preprocessing.preprocessing. preprocessing. preprocessing. Furthermore,Furthermore, Furthermore, Furthermore, Furthermore, to toto to maketomake make make make ourourourourour data datadata data data ready readyready ready ready for forfor for for analysis analysisanalysis analysis analysis, , ,Natural Natural,Natural ,,Natural Natural Language LanguageLanguage Language Language Toolkit ToolkitToolkit Toolkit Toolkit (NLTK) (NLTK)(NLTK) (NLTK) (NLTK) was waswas was was utilized utilizedutilized utilized utilized to toto to removto toremovremov remov removee e e e Appl. Sci. 2021, 11, 6846 6 of 15

Table 3. Schema Used in Experiments.

Unique User ID Aggregated Unique Tweets Region (Mecca/Riyadh)

Table4 shows statistics on the data after data preprocessing. Furthermore, to make our data ready for analysis, Natural Language Toolkit (NLTK) was utilized to remove stop words (Arabic language) and tokenization. Additionally, neutral tweets were omitted, and only tweets showing positive or negative polarity were used.

Table 4. Statistics on the data after preprocessing.

Item Quantity Total number of tweets 35,383 Tweets collected from Riyadh 16,726 Tweets collected from Mecca 18,657

5.2. Lexicon Design The lexicon-based analysis is one of the main approaches used in sentiment analysis [31]. It includes identifying the sentiments of users based on the semantic orientation of their words [32]. This method relies on a dictionary-based approach comprising a list of words according to their semantic orientation. There are different methods of creating such dictionaries, including automatic [33] and manual [34]. For the current study, a bilingual emotion dictionary comprising emotions expressed in the English and Arabic languages was created. This dictionary is named “AEELex” and is based on eight emotions proposed by Plutchik [15] including Fear ( ), Anger ( ), ¬ñk I. « Sadness ( ), Joy ( ), Surprise ( ), Disgust ( ), Trust ( ) and Anticipation ( ). àQk hQ¯ èAg. A®Ó P@ QÖÞ @ é®K I. ¯QK AEELex comprises these eight emotion categories along with two sentiment polarities, i.e., positive (úGAg. @) and negative (úæÊ). Due to . the complexity of the . language used, a manual approach was adopted with the involvement of native Arabic language experts. The emotions fear ( ), anger ( ), ¬ñk I. « disgust (P@ Q Ö Þ @), and sadness (à Qk ) are associated with negative sentiments, whereas joy ( ), trust ( ) and anticipation ( ) fall in the category of positive sentiments. The cat- hQ¯ é®K I. ¯QK egory surprise ( èAg. A®Ó) is a mixed sentiment comprising both positive and negative emotion words. Apart from the words belonging to the eight mood categories, additional words were manually added under the category of positive and negative emotions. We developed our dictionary based on the Moodbook and NRC Word-Emotion Lexicon (Emolex). Both Emolex and Moodbook also tag words according to Plutchik’s eight emotions along with the two categories of semantic polarities—positive and negative. However, neither dictionary categorized the moods according to our context. Moreover, these dictionaries are based on words in the English language only. Therefore, we extended these dictionaries to the Arabic language, along with new words, to form our bilingual dictionary, “AEELex”. This dictionary was developed using more than 19,000 tweets written in the Arabic language. Table5 shows an example of a few mood words in the AEELex dictionary. Appl. Sci. 2021, 11, 6846 7 of 15

Table 5. Example of emotion words in AEELex.

Mood Mood Mood Mood Words Category Category Words Example Sentence (From the Collected Dataset) (English) (English) (Arabic) (Arabic) afraid ¬Am' I J.Ë@ áÓ éªÊ¢ ¬Am' éKA¾Ó øðA AJKYË@ ú¯ m YgñKB darkness . Fear ¬ñk ÐC£ AK Bð ÐC£ Bð èAJ mÌ'@ QÒJ ½ËX ©Óð jË@ úÎ« ÑëY®¯ QK@ð áK YË@ñË@

trembling m 'P@ ' . éK AîDÊË ú æªÖÞ @ ú Gñ m. P@ à@ð úæk wild/brutal ú ækð øQå B@ Y ÑîDJ kð IJ.K ú æË@ HAëñK YJ ®Ë@ éË ÉP war Anger I. « H. Qk ÐC á ÖßAJË@ ÉJ Ëð H. Qk éÊJ Ë ©k. AÖÏ@ èñJ.Ó à@Q g cruel úæ A¯ ÐñJË@ « úæ A¯ ú Î . pain Ñê®JÖÏ@ àAB @ Èñk ZA®J.ÊË ÉJ Ó@ ÕË@ Sadness à Qk ½gðP AëP@ ð ½g. @QÓ PYºK , ½J.Ê¯ ÕË@ , ½¯ðQ£ PY®K ø YË@ àAB @ mistake . é¢Ê« èQÓ ù ªÓ Qå¯ Bð éJÓ AQË@ HXñªK Q®«@ HQj. « ú æºËð é¢Ê« ¡Ê« èQÖÏAëð broke down Qå» ÐAgQË@ Qå» ©Ó ùªJ J.¢Ë@ AK QË@ Qm.k fun hQÓ hQÓ I ¯ð! AJK . AJ ë Joy hQ¯ dance Q × ¯P QåJË@ KP ÈA@ éË Èñ®K ð èYg@ð úÎ« ø ªË@ ¯P h. @Q®Ë@ YJ Ëð ÐQm. éë entertainment éJ ¯QK¡®¯ éJ ¯QË@ éJJ ëð QåJÊË àñºJ K. I. ªÊÖÏAK. ÉgYK h@P AÓ éAK QË@ èP@Pð , ½ÓC¿ iJ m I. Ó

wondrous IJj« IJj« úæ ¬@Qå @ Cª¯ HPAK éKAÒmÌ'@ð ¢ÊË@ é<Ë@ ÈA . . . . . Surprise oh god ' Ì èAg. A®Ó é<ËAK h. AJm àA¿ ð h. AJjÖÏ@ ú æJK éJÓ éJ ¯ Ym 'AÓ PQK. é<ËAK a daemon àA¢J ©ñK éK@ é<Ë@ ú «X@ Õç' @Xð àA¢J Ë@ áÓ é<ËAK. XñªKð ZAñK disgusting ' PQ®Ó PQ®Ó Zú æ úÎ« ®JK@ h. AJm AÓ Disgust Q bullshit Ì P@ ÖÞ @ Z@Që Z@QêË@ @Yë @XAÖÏ m. '@ ú ¯ m disadvantage I. J« ÈAg. QË@ É¿ úÎ« ¼PA¾¯@ ùÒÒªKBI. J«

certain Y»AJÓ @QK áK CÓAêË@ Ó ? Y»AJÓ Trust é®K support Ñ«X éJ Ê« Qº àñJ ÊÓ ©ÊJ .Öß. ú¾ª» YÔg@ áÓ úÍAÓ Ñ«X Õç'Y®K. É¯B@ úÎ«

feeling Ak@ . Q é<Ë àAJJÓB @ Ak@ ú Í@ ZAJ B@ »@ ñK@ ú ¯ AJ@ Qå Q ¯ à@ HA«A @ éJ¯ Èñ®K AJ@ Qå ú¯ PYK ùÔ« YËð prospect ÈAÒJk@ @ñêk. ñJK ÈAÒJk@ ð Q ¢k É¾. ÑëYJ« ðQK A®Ë@ PAK@I. .. I. jJK. Anticipation I¯QK . anxiety Ê¯ AÖß@X ék@QË@ IJ k ÑêÊË@ , Ñë Bð Ê¯ B IJ k ÑêÊË@ feel k@ IJ j@ k@

6. Lexicon Comparison and Validation We compared our bilingual lexicon with two recently proposed emotion lexicons, one in the Arabic language proposed by M. Saad [35] and the second one, an English language lexicon, Moodbook. Table6 highlights the characteristics of these two lexicons along with the proposed AEELex. M. Saad’s Arabic language lexicon comprises six categories of emotions: namely, Fear, Anger, Sadness, Joy, Surprise, and Disgust. However, AEELex has two additional categories i.e., trust and anticipation. Furthermore, AEELex has a comparatively larger number of emotion words than the former. Therefore, the proposed lexicon provides a much more ﬁne-grained study of emotions and can cover many numbers of tweets where the existing lexicons failed to mine any emotions. We performed a comparative analysis by identifying the emotion categories of 1000 tweets in the Arabic language collected from two cities in Saudi Arabia and applying it to AEELex (Arabic version) and the lexicon proposed by M. Saad. It was observed from the results that both lexicons were able to identify all the emotion categories. However, M. Saad’s lexicon was not able to mine emotions from ~60 percent of total tweets; contrastingly, AEELex was not able to mine only 17 percent of total tweets. M. Saad’s lexicon combined several emotions into a single category, which led to a lack of essence for particular emotion words such as Joy, Trust, and Anticipation— emotion categories that were combined into one category (Joy). For example, the emotion Appl. Sci. 2021, 11, 6846 8 of 15

word ” èXAJ.«”(worship) was placed under the joy category by M. Saad, whereas AEELex classiﬁed the same word under the anticipation category rather than joy.

Table 6. Comparison of AEELex with other lexicons.

# Tweets Where Distinct Positive/ Lexicon was Emotion Lexicon # Emotion Categories Negative Not Able to Categories Polarity Find Any Identiﬁed Emotion M. Saad 6 (Fear, Anger, Sadness, 6 No 595 lexicon [35] Joy, Surprise, and Disgust) 8 (M. Saad lexicon + Trust AEELex (Arabic) 8 Yes 177 and Anticipation) 8 (Fear, Anger, Sadness, Moodbook Joy, Surprise, and Disgust, 8 Yes 732 Trust and Anticipation) 8 (Fear, Anger, Sadness, AEELex Joy, Surprise, and Disgust, 8 Yes 663 (English) Trust and Anticipation) # means ‘number of’.

Furthermore, we compared AEELex with an English language lexicon, Moodbook, by analyzing 1000 tweets in the English language collected from Saudi Arabia. Both lexicons have eight emotion categories and were able to identify all the categories in the user tweets. However, it was observed that AEELex outperformed Moodbook by being able to identify a greater number of emotions in the tweets. Thus, we can conclude that AEELex is efﬁcient as a bilingual dictionary in identifying more emotions from text in both English and Arabic.

7. Data Analysis We carried out a study to identify differences in the emotional state of Twitter users residing in Riyadh and Mecca. We aggregated all the tweets of users and analyzed them using AEELex. The analysis was performed in two phases. The ﬁrst phase showed that tweets had the potential to harness their users’ emotions. In the second phase, we highlighted differences in emotions shared by people residing in a religious city or holy city (Mecca) versus a metro city (Riyadh)in Saudia Arabia.

7.1. 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.

PositiveEmotionWords(region) pos = (1) region TotalEmotionWords(region)

NegtiveEmotionWords(region) neg = (2) region TotalEmotionWords(region)

where, posregion and negregion are the positivity and negativity of a region, respectively. PositiveEmotionWords(region), NegativeEmotionWords(region) are the number of positive Appl. Sci. 2021, 11, 6846 9 of 15

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 Figure2 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 God and friendship and positivity such as ” ½J Òm' é<Ë@ . ½J Ê« é<Ë@ ZA AÓ” (As God wills, but God will protect you), “ù®K Y AK J ¯ñJËAK .” (Good luck my friend), “¼QÓ@ QåJ K é<Ë@“ (May Allah make it easy for you), etc. The tweets from Riyadh usually talked about competitive affairs of day-to-day life Appl. Sci. 2021, 11, x FOR PEER REVIEW 10 of 16 such as “ÉJ Ê« ÐñJ Ë@ ñm.Ì'@” (The weather today is ﬁne) and “ÕæÓ ÈAÓ èP@ ùK@” (What kind of

money is this?).

إجابي

)

Positive ( Positive

سلبي

)

Cities under under Study Cities Negative ( Negative

0 10 20 30 40 50 60 70 80 90 100 Percentage of users in the city

Riyadh Mecca

FigureFigure 2 2.. ProportionProportion of of positive positive and and negative negative feelings feelings expressed expressed by by people people residing residing in Mecca and Riyadh.

7.2. Hence,Phase 2: fromPlutchik’s this study,Emotion we-Based could Analysis perceive that user tweets have the potential to show the variable emotions of residents of different areas and consequently the emotional This phase investigated how the emotions of users changed across the two regions. wellbeing of their users. This work thus proffers analysis of user tweets as a method of The varying emotions of the users were observed across different mood categories based perceiving the mental state of a person. on Plutchik’s emotion wheel, as shown in Figure 3. Figure 4 illustrates how the proportion 7.2.of user Phase moods 2: Plutchik’s differed Emotion-Based across the two Analysis regions. In the category of the most expressed emo- tionThis, which phase was investigated Anticipation how, the theusers emotions from Mecca of users were changed ahead across(50%) thein comparison two regions. to هللا Thethose varying from emotionsRiyadh (around of the users 40% were). The observed users in across Mecca different tended moodto use categories words such based as “ on ” استقبال of الضيوف الشيخthe proportion عبدهللا how الغنيم يرحمه Plutchik’smeaning emotion"God suffices wheel," in as their shown tweets in Figure: for example3. Figure,4 “ illustrates moods(Reception differed of guest across Sheikh the two Abdullah regions. Al In-Ghunaim, the category may of theGod most have expressed mercy on emotion, him). As ”هللاuser whichshown was in theAnticipation plot, users, the from users both from the Mecca regions were Riyadh ahead and (50%) Mecca in comparisonexperienced toJoy those, but a fromhigher Riyadh proportion (around of 40%). the users The users in Mecca in Mecca, approximately tended to use 20%, words expressed such as the “ emotion” meaning Joy. :love), etc. iné<Ë@ their tweets) ”حب“ ,(flower) ’’ورد“ In this city, the users use words such as "God sufﬁces" in their tweets: for example, “ ” -Ë@ roses) ÈAJ.®J@ ex Ë@was¬ñJ blue JªË@ é<Ë@YJ that.« qJit imaginedÕæ é<Ë@ (I just éÔgQK” انا بس تخيلت انه ورد ازرق روقت” ,for example ,on ( I him).appreciate As shown deep ”انا اقدرhave mercy الصداقات God العميقه may جميله مليانه حب“,Receptionpressing positivity of guest Sheikh and happiness, Abdullah Al-Ghunaim) inbeautiful the plot, friendships users from full both of the love), regions etc. Riyadh and Mecca experienced Joy, but a higher -was the one most ex (حزن) For emotions falling in the negative categories, Sadness pain), etc. to) "الم" ,(tired) ”تعبان“ pressed. Users from both regions used words such as I am) ”تعبان شكلها فره التيك توك قبل النوم“ express sad emotions; for example, tweets such as ال تكن المضحي دائما ، “ ,(tired, it seems that because I spent a long time on Tik Tok before bed Don't always bear the pain, pass by, be the bad person for) ”تمرد ، كن الشخص السيء لمره واحده -was the second most expressed emo (غضب) once) have been seen on user profiles. Anger hungry) in) ”جوع“ ,(crime) ”جريمه“ tion in this category, articulated with phrases such as Late)”جوع آخر الليل ماندري وش نسوي فيه“ ,(stealing is a crime)”السرقة جريمة“ tweets such as expressed with words such (اشمئزاز) night hunger, what do we do?), followed by Disgust ”عيب عيب محتشمه الترسموها زي كذا“ petty), etc., in tweets such as)”تافه“ ,(shameful)”عيب“ as Petty, I could) ”تافهة كان يمكنني فعل اكثر ما تترقب“ ,(Shame, shameful, don't draw it like this) have done more than you expect). It was also shown that the proportion of users falling into these negative categories was much higher in Riyadh than Mecca. These persistently negative feelings inculcated in a population can lead to psychological issues such as bipolar disorders, depression, etc. It has been reported that psychological ailments such as drug use, depression, and anxiety-based disorders are among the leading causes of disability in Saudi Arabia [30]. Furthermore, according to a study [36], as drugs, petrol, lighter fluids, and tobacco were considered to be easily accessible to young generations in the city of Riyadh. This could be one of the reasons why the people of Riyadh show more negative and depressive emotions in their tweets in comparison to people in Mecca city. Appl. Sci. 2021, 11, 6846 10 of 15

proportion of the users in Mecca, approximately 20%, expressed the emotion Joy. In this city, Appl. Sci. 2021, 11, x FOR PEER REVIEWthe users use words such as “ ” (ﬂower), “ ” (love), etc. in their tweets: for example, ” 11 of 16 Appl. Sci. 2021, 11, x FOR PEER REVIEW XPð I. k 11 of 16 I ¯ðP PP@ XPð éK@ IÊJ m' . AK@ ” (I just imagined that it was blue roses) expressing positivity and happiness, “Ik éKAJ ÊÓ éÊJÔg é®J ÒªË@ HA ¯@YË@ PY¯@ AK@ ” ( I appreciate deep, beautiful . . friendships full of love), etc.

. . FFigureigure 3 3.. EightEight emotion emotion words words [37] [37].. Figure 3. Eight emotion words [37]. 60 6050 5040 4030 3020 10 20

0 Percentage of Users Percentage Sadness Surprise خوف( ) Disgust Anticipation Fear ثقة()Anger Trust فرح() Joy 10

متفاجىء() حزن() توقع() اشمئزاز() غضب() 0 Percentage of Users Percentage Sadness Surprise خوف( ) Disgust Anticipation Fear ثقة()Anger Trust فرح() Joy متفاجىء() حزن() توقع()Category اشمئزاز()Emotion غضب() Mecca Riyadh Emotion Category Figure 4. The proportion of user moods across Riyadh and Mecca. Figure 4. The proportion of Meccauser moodsRiyadh across Riyadh and Mecca.

8. DiscussionsFor emotions falling in the negative categories, Sadness (à Qk ) was the one most expressed. Figure 4. The proportion of user moods across Riyadh and Mecca. UsersIn fromthis paper, both regions we made used use words of social such media as “àAJ platforms.ªK” (tired), such “ÕË@” as (pain), Twitter etc. to tostudy express the emotionssad emotions of people; for example, residing tweetsin a holy such city as versus “ÐñJË@ th ÉJose¯ ¼ñ residingK ½JJË@ èQ in¯ AêÊ¾a typical àAJ ª Kmetropolitan” (I am tired, 8.center Discussions in the same country. Twitter was used as a choice. of social media in. this study, as it seems that because I spent a long time on Tik Tok before bed), “ this Inwork this was paper, focused we on made the extraction use of social of sentiments media platforms (emotions), such XQÖ fromß , as AÖtext.ß@X Twitter ú jTheÖ Ï@Twitter áºto KBstudy the network appeared to be one ” of (Don’t the main always sources bear of the text pain, through pass by,which be theusers bad express person their for emotionsèYg@ð èQÖ ÏofZú peopleæË@ j residingË@ á» in a holy city versus those residing in a typical metropolitan centerfeelingonce) havein in the just been same 280 seen characters. country. on userproﬁles. TOwitterther socialAnger was networking(usedI « )as was a choice theplatforms second of social mostsuch expressedasmedia Facebook in emotion this hold study , as other user content (audios, videos, images in. almost equal ratios) as well. Although the thisin thiswork category, was focused articulated on withthe extraction phrases such of as sentiments “ ” (crime), (emotions) “ ” (hungry) from text. in tweetsThe Twitter inclusion of all the different types of media contentéÖß wasQk. beyond the¨ñk scope. of this study, it network appeared to be one of the main sources of text through which users express their cousuchld as be “ used in our”(stealing future work. is a crime),To study “ difference s in user emotions between”(Late the night two feeling inéÖ justß Qk. 2é¯QåË@80 characters. Other socialéJ ¯ ø networkingñ ð ø PY platformsKAÓ ÉJ ÊË@ Qk such@ ¨ñk . as Facebook hold citieshunger,, we whatperformed do we various do?), followed experiments by Disgust and conducted( Q ) expresseda comparative with study words based such on as otherusers userin these content cities. (audios,To perform videos, this comparative images in almoststudy,P@ ÖÞ @ we equal considered ratios) tweetsas well. posted Although by the inclusion“ ”(shameful), of all the “ different ”(petty), types etc., of in media tweets suchcontent as “ was beyond the scope × of this study” , it usersI. J« in Mecca (pilgrimageé¯AK region) and Riyadh (typical metropolitan@Y» ø P AëñÖÞQ KBcity). éÒ m I. J«I. J« cou(Shame,ldOur be shameful,usedfindings in our indicate don’t future draw that work. it people like this),To residing study “ differencein Mecca ands in Riyadh user emotions have ”significant (Petty, between I could dif- the two I. ¯QK AÓ Q»@ Éª¯ úæJºÖß àA¿ éê¯AK citiesferencehave, donewes in performed moretheir emotional than youvarious expect).states. experiments The It was entire also studyand shown conducted was that divided the proportiona comparativeinto two ofphases. users study fallingIn the based on usersfirst phase,in these we cities. concluded To perform that tweets this posted comparative by users study, have thewe potentialconsidered to reveal tweets their posted by usersexpressed in Mecca emotions (pilgrimage and thus region)their mental and stateRiyadhs. People (typical from metropolitan holy areas expressed city). much moreOur posi findingstivity than indicate citizens that from people a typical residing city. The in Meccahectic andand stressful Riyadh lifehave of significantthe city dif- ferenceaffects s people in their negatively emotional, leading states. to The comparatively entire study more was psychological divided into disorders two phases. and In the first phase, we concluded that tweets posted by users have the potential to reveal their expressed emotions and thus their mental states. People from holy areas expressed much more positivity than citizens from a typical city. The hectic and stressful life of the city affects people negatively, leading to comparatively more psychological disorders and Appl. Sci. 2021, 11, 6846 11 of 15

into these negative categories was much higher in Riyadh than Mecca. These persistently negative feelings inculcated in a population can lead to psychological issues such as bipolar disorders, depression, etc. It has been reported that psychological ailments such as drug use, depression, and anxiety-based disorders are among the leading causes of disability in Saudi Arabia [30]. Furthermore, according to a study [36], as drugs, petrol, lighter ﬂuids, and tobacco were considered to be easily accessible to young generations in the city of Riyadh. This could be one of the reasons why the people of Riyadh show more negative and depressive emotions in their tweets in comparison to people in Mecca city.

8. Discussions In this paper, we made use of social media platforms such as Twitter to study the emotions of people residing in a holy city versus those residing in a typical metropolitan center in the same country. Twitter was used as a choice of social media in this study, as this work was focused on the extraction of sentiments (emotions) from text. The Twitter network appeared to be one of the main sources of text through which users express their feeling in just 280 characters. Other social networking platforms such as Facebook hold other user content (audios, videos, images in almost equal ratios) as well. Although the inclusion of all the different types of media content was beyond the scope of this study, it could be used in our future work. To study differences in user emotions between the two cities, we performed various experiments and conducted a comparative study based on users in these cities. To perform this comparative study, we considered tweets posted by users in Mecca (pilgrimage region) and Riyadh (typical metropolitan city). Our findings indicate that people residing in Mecca and Riyadh have significant differences in their emotional states. The entire study was divided into two phases. In the Appl. Sci. 2021, 11, 6846 12 of 16 first phase, we concludedAppl. that Sci. 2021 tweets, 11, 6846 posted by users have the potential to reveal their 12 of 16 Appl. Sci. 2021, 11, 6846 12 of 16 Appl. Sci. 2021, 11, 6846 Appl. Sci. 2021, 11, 6846 expressed emotions and thus their mental12 states. of 16 People from holy12 ofareas 16 expressed much more positivity than citizens from a typical city. The hectic and stressful life of the city affects negative feelings such as anger, disgust, and sadness expressed in words such as people negatively, leading to comparatively more psychologicalnegative feelings disorders such as and anger, negative disgust, and sadness expressed in words such as .(petty)”ﺗافه“ pain), or) ” الم”,(shameful)”عيﺐ“ .(petty)”ﺗافه“ pain),” (shameful), or) ” “ المin such words words”(shameful),” as such as as عيﺐnegativenegative feelings feelings such feelings such as asanger, such anger, asdisgust, anger, disgust, and disgust, sadness and and sadness expressed sadness expressed expressed in words “ in negative feelings such as anger, disgust, and sadness expressed in words such as Conversely, people residing in religious places such as Mecca, one of the holiest cities petty). (petty). Conversely, people residing in religious places such as Mecca, one of the holiest)” ﺗافهﺗافه““ pain),(pain), oror) ”” المالم“”,(shameful),””(shameful)”عيﺐعيﺐ““ petty). in the world for Muslims, are much more at ease and at peace with their life. They tend to)”ﺗافه“ pain), or) ” الم”,(shameful)”عيﺐ“ Conversely,Conversely, people Conversely, residing residing in in people religious religious residing places places in such religious such as Mecca, as places Mecca, one such cities oneof the as ofin Mecca,holiest thethe holiestworld cities one of for the Muslims, holiest cities are much more at ease and at peace with their life. They Conversely, people residing in religious places such as Mecca, one of the holiest turn to God (Allah) when in need and as a result shut out their negative emotions. Hence, citiesin the in world the world for Muslims,in for the Muslims, world are much for are Muslims, muchmore atmore areease muchat and ease at more andpeace at at ease withpeace and their withtend at life. peace their toThey turn withlife. tend They their to to God life. (Allah) They tend when to in need and as a result shut out their negative cities in the world for Muslims, are much more at ease and at peace with their life. They they are much happier and have a positive outlook on life. They tend to use spiritual tend to turn to God tendturn (Allah) to to God whenturn (Allah) to in Godturn need when to (Allah) and God in need as (Allah) when a and result when inas a need shutresult in need and out shut and as their out a as their result negative a result negative shut shut outemotions. outemotions. their their negativeHence, Hence, they emotions. are much Hence, happier and have a positive outlook on life. They tend to alhamdulillah meaning thanks to Allah), and) ”الحمد“ ,(Allah)” ﷲ”words such as ,(alhamdulillah meaning thanks to Allah) ”الحمد“ ,(Allah)” ﷲemotions. Hence, they emotions.theyare much are much happierHence, happier they and arehave are and much much a have positive happier happier a positive outlook and and haveoutlook haveon life. a positive a They positiveon life. tend outlook They outlook to tend onuse onlife. to spiritual life. Theyuse Theyspiritual tend words tend to such to use as” spiritual .ya Rabb meaning O Lord) revealing their emotional connection with god)”يارب“ .Allah), thanksto”(ya Allah), Rabb and to Allah), meaning and O “ Lord)” revealing their emotional connection with god يارب“alhamdulillahÌ ” (alhamdulillah meaning meaning thanks meaningand thanks to) ”“ الحمدAllah), “ (alhamdulillah) ””الحمد“ ,(as “”(Allah ﷲ”such”(Allah), suchas الحمدwordsﷲ”such words as ﷲusewords spiritual use spiritual words such as” ”(Allah), “ ” (alhamdulillahé<Ë@ meaningé<ËYÒm thanks'@ to Allah), In the secondH. PAK phase, we performed a fine-grained analysis of user emotions and stud- ya”(ya Rabb Rabb meaning(ya meaning Rabb O meaningLord) O Lord) revealing O revealing Lord) their revealing their emotional emotional their connection emotional connection connectionwith withIn god. the god. with second god. phase, we performed a fine-grained analysis of user emotions and)”يارب“ يارب“and ya Rabb meaning O Lord) revealing their emotional connection with god. ied the differences in emotions shared by people residing in the two cities. We observed)”يارب“ and InIn the the second second phase, phase,In we the we performed second performed phase, a fine-grained a fine-grained we performed analysis analysis a fine-grained of user of useremotionsstud-ied emotions analysis theand differencesstud- of and user emotions in emotions and shared by people residing in the two cities. We In the second phase, we performed a fine-grained analysis of user emotions and that words in the positive categories such as Joy and Anticipation were used by a higher stud-iedied the differences the differencesstudied in emotions in the emotions differences shared sharedby in people emotions by peopleresiding shared residing in by the people two in residingcities. theobserved two We in cities. thatobserved the two words We cities. in the We observedpositive categories such as Joy and Anticipation were used by stud-ied the differences in emotions shared by people residing in the two cities. We proportion of people in Mecca (~90%) compared to those in Riyadh (~70%). In order to observedthat words that in wordsthe thatpositive in words the categories positive in the positivecategories such as categories Joy such and as Anticipation Joy such and as Anticipation Joy andwerea Anticipation higherused were by proportion aused higher were by used of people by a higher in Mecca (~90%) compared to those in Riyadh (~70%). In observed that words in the positive categories such as Joy and Anticipation were used by perform a more fine-grained analysis, we created a word-cloud of the words extracted aproportion higher proportion of peopleproportion of in people Mecca of in (~90%) people Mecca incompared (~90%) Mecca compared (~90%) to those compared toin thoseRiyadh toin (~70%).order thoseRiyadh into In (~70%). Riyadh perform order In (~70%).to a more In fine-grained order to analysis, we created a word-cloud of the words a higher proportion of people in Mecca (~90%) compared to those in Riyadh (~70%). In from the tweets of people residing in Mecca and Riyadh cities, as shown in Figures 5 and orderperform to performa more fine-grained perform a more fine-grained a more analysis, fine-grained analysis,we created analysis, we a createdword-cloud we createda word-cloud of the aextracted word-cloud words of the extracted from words of the wordstweets extracted of people residing in Mecca and Riyadh cities, as shown in order to perform a more fine-grained analysis, we created a word-cloud of the words 6, respectively. For a better understanding of user emotions, the top five words in each of extracted from the tweetsextractedfrom of the people tweets from residing theoffrom people tweets the in residing tweets ofMecca people of andin people Mecca residing Riyadh residing and in cities, Riyadh Mecca in as Mecca cities, andshown and Riyadh as in Riyadhshown cities,Figures in cities, Figures as 5 asshown and shown 5 6,and inrespectively. in Figures5 andFor6 a, better understanding of user emotions, the top five the emotion categories expressed by users in the two cities are presented in Table 7. It can Figures 5 and 6, respectively.Figures6, respectively. 5For and a better6, For respectively.respectively. a understandingbetter understanding For a betterofbetter user understandingofunderstanding emotions, user emotions, the of topof user theuser five top emotions, emotions, fivewords words the the in top in top each each five five words of the in emotion each of the categories expressed by users in the two cities are meaning) ”خوف“ be seen that some of the words used are common in both cities, such as words in each of thewordsthe emotion emotion in each categories categories ofemotion the expressed expressed emotion categories categoriesby users expressed users in in expressedthe the by two users two cities by in cities are the users presented two are in cities thepresented in are two Table presented cities in7. ItTable can are in Table 7. It can7. It be can seen be that some of the words used are common in both -salvation/finish) and rank at the topmost position in their correspond) ”خﻼص“ fear) and salvation/finish) and rank at the) ”خﻼص“ meaning ” (meaning fear) fear) and) ” خوف“ such such” (meaning in as both خوف“presented in Table 7. presentedItbe canseen be that seen in someTable thatseen of 7.some the It that canwords of some thebe used seen ofwords the arethat words used common some areused of common in arethe both commonwords cities, in both used in such both are ascities, cities, common .salvation/finish) and rank at the topmost position in their correspond-ing categories, i.e., fear and anger, respectively) ”خﻼص“ fear) and .and and” (salvation/finish) rank rank at at the the topmost topmost and position rank position at in the their in their corresponding correspond-ing categories, i.e., fear and anger, respectively خﻼص“ meaning ”” (salvation/finish) (salvation/finish) fear) and) خﻼص ““ ” andخوفcities, (meaning such fear) as “ and ”خوف“ cities, such as ing categories, i.e., fear and anger, respectively. topmost position in theirtopmost correspond-ing position incategories, categories, their correspond-ing i.e., i.e., fear fear and and categories, anger, anger, respectively. respectively. i.e., fear and anger, respectively.

Figure 5. Word-cloud of Mecca users. Figure 5. Word-cloud of Mecca users. Figure 5. Word-cloud of Mecca users. Figure 5. Word-cloud of MeccaFigure users.5. Word-cloud of Mecca users. Appl. Sci. 2021, 11, 6846 12 of 15

Table 7. Top 5 words from each emotion category. Table 7. Top 5 words from each emotion category.

Mood Category Riyadh Riyadh (English) Mecca Mecca (English) fear ﺧﻮﻑ fear ﺧﻮﻑ genie ﺟﻦ scared ﺧﺎﻳﻒ scared ﺧﺎﻳﻒ worry ﻗﻠﻖ Fear are afraid ﻳﺨﺎﻑ are afraid ﻳﺨﺎﻑ yore ﺟﻮﺭ I'm afraid ﺃﺧﺎﻑ Salvation/finish ﺧﻼﺹ Salvation/finish ﺧﻼﺹ (bitter (noun ﻣﺮ difficult ﺻﻌﺐ difficult ﺻﻌﺐ (bitter (noun ﻣﺮ Anger expensive ﻏﺎﻟﻲ stupid ﻏﺒﻲ fault ﻋﻴﺐ hate ﻛﺮﻩ worry ﻫﻢ worry ﻫﻢ pain ﺍﻟﻢ tightened ﺷﺪ feeling ﺷﻌﻮﺭ pain ﺍﻟﻢ Sadness sad ﺣﺰﻥ feeling ﺷﻌﻮﺭ lost ﻓﻘﺪ request ﻃﻠﺐ Love ﺣﺐ Thank God ﺍﻟﺤﻤﺪﻟﻠﻪ sweet ﺣﻠﻮ sweet ﺣﻠﻮ Thank God ﺍﻟﺤﻤﺪﻟﻠﻪ love ﺣﺐ Joy heart ﻗﻠﺐ new ﺟﺪﻳﺪ new ﺟﺪﻳﺪ heart ﻗﻠﺐ O God ﻳﺎﻟﻠﻪ O God ﻳﺎﻟﻠﻪ Oh man ﻳﺎﺷﻴﺦ defense ﺩﻓﺎﻉ O mommy ﻳﻤﻪ seriously ﻣﻌﻘﻮﻟﻪ Surprise seriously ﻣﻌﻘﻮﻟﻪ O mommy ﻳﻤﻪ defense ﺩﻓﺎﻉ Oh Man ﻳﺎﺷﻴﺦ fault ﻋﻴﺐ yea ﻳﻊ yea ﻳﻊ stupid ﻏﺒﻲ stupid ﻏﺒﻲ fault ﻋﻴﺐ Disgust ugh ﻗﺮﻑ ugh ﻗﺮﻑ mocked ﺳﺨﺮ worry ﻗﻠﻖ myself ﻧﻔﺴﻲ Amen ﺍﻣﻴﻦ Done ﺗﻢ Done ﺗﻢ Amen ﺍﻣﻴﻦ believe ﺻﺪﻕ Trust patience ﺍﻟﺼﺒﺮ secret ﺳﺮ believe ﺻﺪﻕ myself ﻧﻔﺴﻲ God ﷲ God ﷲ time ﻭﻗﺖ time ﻭﻗﺖ thank you ﺷﻜﺮﺍ peace ﺳﻼﻡ Anticipation soul ﺭﻭﺡ thank you ﺷﻜﺮﺍ same ﻧﻔﺲ I feel ﺍﺣﺲ

Appl. Sci. 2021, 11, x FOR PEER REVIEW 12 of 16

negative feelings such as anger, disgust, and sadness expressed in words such as d”(petty).isgust, and sadness expressed in words such as تافه“ such ” (pain), as anger, or الم shameful),” feelings)”عيب“negative religious”(petty). places such as Mecca, one of the holiest cities تافهpain), residing or “ in) ” المConversely,shameful),” people)”عيب“ in theConversely, world for Muslims people residing, are much in religiousmore at ease places and such at peace as Mecca, with one their of life. the Theyholiest tend cities to inturn the to world God (Allah)for Muslims when, arein need much and more as a at result ease andshut at out peace their with negative their emotions.life. They tendHence, to turnthey toare God much (Allah) happier when and in need have and a positive as a result outlook shut out on theirlife. Theynegative tend emotions. to use spiritual Hence, a positive” (alhamdulillah outlook on meaning life. They thanks tend to to use Allah), spiritual and الحمدهلل“ Allah), and have)” هللاtheywords are such much as” happier revealing” (alhamdulillah their emotional meaning connection thanks with to god. Allah), and الحمدهلل(Allah), O Lor “d)” هللاya such Rabb as” meaning)”يارب“words -In”(ya the Rabb second meaning phase, Owe Lor performedd) revealing a fine their-grained emotional analysis connection of user emotions with god. and studيارب“ ied theIn thedifference seconds phase, in emotions we performed shared bya fine people-grained residing analysis in the of usertwo cities.emotions We andobserved stud- iedthat the words difference in the spositive in emotions categor sharedies such by peopleas Joy andresiding Anticipation in the two were cities. used We by observed a higher thatproportion words inof thepeople positive in Mecca categor (~ies90%) such compared as Joy and to thoseAnticipation in Riyadh were (~ 70%)used. byIn aorder higher to proportionperform a moreof people fine- grainedin Mecca analysis, (~90%) comparedwe created to a thoseword -incloud Riyadh of the (~70%) words. In extracted order to performfrom the atweets more offine people-grained residing analysis, in Mecca we created and Riyadh a word cities-cloud, as shownof the wordsin Figures extracted 5 and from6, respectively. the tweets Forof people a better residing understanding in Mecca of and user Riyadh emotions, cities the, as top shown five wordsin Figures in each 5 and of 6the, respectively. emotion categories For a better expressed understanding by users inof theuser two emotions, cities are the presented top five wordsin Table in 7.each It can of Table” (meaning 7. It can خوفthebe seenemotion that categoriessome of the expressed words used by users are common in the two in citiesboth citiesare presented, such as “in correspond-” (meaning13 of 15 خوف“some” (ofsalvation/finish) the words used and are rankcommon at the in topmost both cities position, such inas their خالﺹ“ Appl. Sci. 2021, 11, 6846 befear) seen and that -i.e.” (, sfalvation/finish)ear and anger, respectively. and rank at the topmost position in their correspond ,خالﺹ“ fear)ing categories and ing categories, i.e., fear and anger, respectively.

FigureFigure 5.5.Word-cloud Word-cloud of of Mecca Mecca users. users. Figure 5. Word-cloud of Mecca users.

Figure 6. Word-cloud of Riyadh users. Figure 6. Word-cloud of Riyadh users. Figure 6. Word-cloud of Riyadh users. 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 AEELex based on two recently proposed emotion lexicons (M. Saad’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). Plutchik’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 Appl. Sci. 2021, 11, 6846 14 of 15

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.

Author Contributions: K.A.S. and M.A.W. conceived and designed the experiments; K.A.S. performed the experiments; K.A.S., K.T. and F.S.A. created the bilingual lexicon., K.A.S. and M.A.W. analyzed the data; K.A.S. prepared the first draft of the paper with F.S.A. as Arabic language expert; M.A.W. and K.T. edited the paper; M.A.W. proofread the paper and supervised the overall work. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Program of Research Project Funding after Publication. Grant No. (41-PRFA-P-19). Institutional Review Board Statement: The study was conducted according to the guidelines of the Institutional Review Board (IRB) at Princess Nourah bin Abdulrahman University, Riyadh, Saudi Arabia. Informed Consent Statement: Not applicable. Data Availability Statement: 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/ 3jku4En, accessed on 23 July 2021) under the title of the paper. Conflicts of Interest: The authors declare no conflict of interest.

References 1. Ulrich, R.S.; Simons, R.F.; Losito, B.D.; Fiorito, E.; Miles, M.A.; Zelson, M. Stress recovery during exposure to natural and urban environments. J. Environ. Psychol. 1991, 11, 201–230. [CrossRef] 2. Fitri, V.A.; Andreswari, R.; Hasibuan, M.A. Sentiment analysis of social media Twitter with case of Anti-LGBT campaign in Indonesia using Naïve Bayes, decision tree, and random forest algorithm. Procedia Comput. Sci. 2019, 161, 765–772. [CrossRef] 3. Kušen, E.; Strembeck, M. Politics, sentiments, and misinformation: An analysis of the Twitter discussion on the 2016 Austrian Presidential Elections. Online Soc. Netw. Media 2018, 5, 37–50. [CrossRef] 4. Gmi_blogger. Saudi Arabia Social Media Statistics 2020 (Infographics)-GMI Blog. Infographics, Social Media Marketing. 2020. Available online: https://www.globalmediainsight.com/blog/saudi-arabia-social-media-statistics/ (accessed on 28 November 2020). 5. Strapparava, C.; Valitutti, A. WordNet-Affect: An Affective Extension of WordNet. In Proceedings of the LREC, Lisbon, Portugal, 24–30 May 2004. 6. Esuli, A.; Sebastiani, F. Sentiwordnet: A Publicly Available Lexical Resource for Opinion Mining. In Proceedings of the 5th International Conference on Language Resources and Evaluation, Genoa, Italy, 22–28 May 2006. 7. Basiri, M.E.; Nemati, S.; Abdar, M.; Cambria, E.; Acharrya, U.R. ABCDM: An Attention-based Bidirectional CNN-RNN Deep Model for sentiment analysis. Future Gener. Comput. Syst. 2021, 115, 279–294. [CrossRef] 8. Adikari, A.; Gamage, G.; de Silva, D.; Mills, N.; Wong, S.-M.J.; Alahakoon, D. A self structuring artificial intelligence framework for deep emotions modelling and analysis on the social web. Future Gener. Comput. Syst. 2020, 116, 302–315. [CrossRef] 9. Liu, P.; Chen, W.; Ou, G.; Wang, T.; Yang, D.; Lei, K. Sarcasm detection in social media based on imbalanced classification. In Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Springer: Cham, Switzerland; Volume 8485, pp. 459–471. 10. Li, J.; Ni, S.; Kao, H.-Y. Birds of a Feather Rumor Together? Exploring Homogeneity and Conversation Structure in Social Media for Rumor Detection. IEEE Access 2020, 8, 212865–212875. [CrossRef] 11. Shen, G.; Jia, J.; Nie, L.; Feng, F.; Zhang, C.; Hu, T.; Chua, T.-S.; Zhu, W. Depression Detection via Harvesting Social Media: A Multimodal Dictionary Learning Solution. In Proceedings of the IJCAI, Melbourne, Australia, 19–25 August 2017. 12. De Choudhury, M.; Gamon, M.; Counts, S.; Horvitz, E. Predicting Depression via Social Media. In Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media; Association for the Advancement of Artificial Intelligence: Menlo Park, CA, USA, 2013. 13. Nguyen, T.; Phung, D.; Dao, B.; Venkatesh, S.; Berk, M. Affective and content analysis of online depression communities. IEEE Trans. Affect. Comput. 2014, 5, 217–226. [CrossRef] Appl. Sci. 2021, 11, 6846 15 of 15

14. Ma, Y.; Cao, Y. Dual Attention based Suicide Risk Detection on Social Media. In Proceedings of the 2020 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2020, Dialian, China, 27 June 2020; pp. 637–640. 15. Plutchik, R. Emotions: A general psychoevolutionary theory. Approaches Emot. 1984, 1984, 197–219. 16. Wani, M.A.; Agarwal, N.; Jabin, S.; Hussain, S.Z. User emotion analysis in conflicting versus non-conflicting regions using online social networks. Telemat. Informatics 2018, 35, 2326–2336. [CrossRef] 17. Mohammad, S.M.; Turney, P.D. Crowdsourcing a word-emotion association lexicon. Comput. Intell. 2013, 29, 436–465. [CrossRef] 18. Li, D.; Rzepka, R.; Ptaszynski, M.; Araki, K. HEMOS: A novel deep learning-based fine-grained humor detecting method for sentiment analysis of social media. Inf. Process. Manag. 2020, 57, 102290. [CrossRef] 19. Park, S.B.; Kim, J.; Lee, Y.K.; Ok, C.M. Visualizing theme park visitors’ emotions using social media analytics and geospatial analytics. Tour. Manag. 2020, 80, 104127. [CrossRef] 20. Bagozzi, R.P.; Gopinath, M.; Nyer, P.U. The role of emotions in marketing. J. Acad. Mark. Sci. 1999, 27, 184–206. [CrossRef] 21. Loureiro, M.L.; Alló, M. Sensing climate change and energy issues: Sentiment and emotion analysis with social media in the U.K. and Spain. Energy Policy 2020, 143, 111490. [CrossRef] 22. Ghallab, A.; Mohsen, A.; Ali, Y. Arabic Sentiment Analysis: A Systematic Literature Review. Appl. Comput. Intell. Soft Comput. 2020, 2020, 7403128. [CrossRef] 23. Hawalah, A. Semantic ontology-based approach to enhance arabic text classification. Big Data Cogn. Comput. 2019, 3, 53. [ CrossRef] 24. Abdul-Mageed, M.; Diab, M. SANA: A large scale multi-genre, multi-dialect lexicon for Arabic subjectivity and sentiment analysis. In Proceedings of the 9th International Conference on Language Resources and Evaluation, LREC 2014, Reykjavik, Iceland, 26–31 May 2014; pp. 1162–1169. 25. Al-Saggaf, Y.; Simmons, P. Social media in Saudi Arabia: Exploring its use during two natural disasters. Technol. Forecast. Soc. Change 2015, 95, 3–15. [CrossRef] 26. Al-Khatib, A.; El-Beltagy, S.R. Emotional tone detection in Arabic tweets. In Proceedings of the Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Budapest, Hungary, 10 October 2018; Volume 10762, pp. 105–114. 27. Tweepy. Available online: https://www.tweepy.org/ (accessed on 1 April 2021). 28. Klokantech. Bounding Box Tool: Metadata Enrichment for Catalogue Records by Visually Selecting Geographic Coordinates (Latitude/Longitude) for Maps. Available online: https://boundingbox.klokantech.com/ (accessed on 29 October 2020). 29. Gutub, A.; Ghouti, L.; Elarian, Y.; Awaida, S.; Alvi, A. Utilizing Diacritic Marks for Arabic Text Steganography. Kuwait J. Sci. Eng. 2010, 37, 89–109. 30. Abandah, G.A.; Graves, A.; Al-Shagoor, B.; Arabiyat, A.; Jamour, F.; Al-Taee, M. Automatic diacritization of Arabic text using recurrent neural networks. Int. J. Doc. Anal. Recognit. 2015, 18, 183–197. [CrossRef] 31. Taboada, M.; Brooke, J.; Tofiloski, M.; Voll, K.; Stede, M. Lexicon-basedmethods for sentiment analysis. Comput. Linguist. 2011, 37, 267–307. [CrossRef] 32. Jurek, A.; Mulvenna, M.D.; Bi, Y. Improved lexicon-based sentiment analysis for social media analytics. Secur. Inform. 2015, 4, 9. [ CrossRef] 33. Turney, P.D.; Littman, M.L. Measuring praise and criticism: Inference of semantic orientation from association. ACM Trans. Inf. Syst. 2003, 21, 315–346. [CrossRef] 34. Tong, R. An operational system for detecting and tracking opinions in on-line discussions. In Proceedings of the Working Notes of the SIGIR Workshop on Operational Text Classification, New York, NY, USA, 13 September 2001; pp. 1–6. 35. Saad, M. Mining Documents and Sentiments in Cross-Lingual Context; Université de Lorraine: Metz, France, 15 February 2015. 36. Koenig, H.G.; Al Zaben, F.; Sehlo, M.G.; Khalifa, D.A.; Al Ahwal, M.S.; Qureshi, N.A.; Al-Habeeb, A.A. Mental Health Care in Saudi Arabia: Past, Present and Future. Open J. Psychiatry 2014, 4, 113–130. [CrossRef] 37. Plutchik, R. The Emotions; University Press of America: Lanham, MD, USA, 1991.