## 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] users' 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.