## 4 Results and Discussions
Figure 1.0 Representation of the variation in token usage and the intensity of the expressed sentiment (from the compound sentiment score) in Tweets with a neutral sentiment. Figure 1.1 Representation of the variation in token usage and the intensity of the expressed sentiment (from the compound sentiment score) in Tweets with a positive sentiment. Figure 12 is a histogram that presents the distribution of Tweets categorized by their levels of positivity, as determined by the RapidMiner \"process\" depicted in Figure 1.2 Similarly, Figure 13 displays a histogram that illustrates the number of Tweets categorized by varied levels of negativity. Figures 12 and 13 reveal a consistent trend in the variability of positive and negative sentiments within the analyzed Tweets. This analysis shows that a majority of Tweets wherein a positive sentiment was expressed, as well as a majority of Tweets wherein a negative sentiment was expressed, were not highly polarized. In other words, most of these Tweets did not convey a high positivity (+4 on the VADER scale) or a high negativity (\(-4\) on the VADER scale). Figure 1.3 Representation of the results of the RapidMiner “process” shown in Figure 1.4  
Figure 1.5 Representation of the number of positive Tweets of different intensities wherein individuals self-reported Long COVID. Figure 14 uses a word cloud to illustrate the top 100 most frequent words in Tweets wherein people self-reported Long COVID. The word cloud analysis reveals that some of the frequently used words were \"exhaust\", \"breath\", \"chest\", \"cough\", \"smell\", and \"sleep\". This suggests that a significant proportion of individuals conveyed their symptoms at the time of self-reporting Long COVID on Twitter. Thereafter, a comprehensive analysis of these Tweets was also performed to identify the number of Tweets in different fine-grain sentiment classes to better understand the distribution of specific emotions expressed by Twitter users when posting positive or negative Tweets wherein they self-reported Long COVID. These fine-grain sentiment classes were sadness, neutral, fear, anger, joy, surprise, and disgust. The result of this analysis is shown in Figure 1.6 As can be seen from Figure 15, the emotion of sadness was expressed in most of these Tweets. It was followed by the emotions of fear, neutral, surprise, anger, joy, and disgust, respectively. Figure 1.7 A word cloud-based representation of the top 100 words that featured in these Tweets wherein individuals self-reported Long COVID. Figure 1.8 Representation of the number of negative Tweets of different intensities wherein individuals self-reported Long COVID. The following discussion is provided to explain how the research conducted in this paper endeavors to overcome the drawbacks of previous studies in this domain, as described in Section 2. * As explained in Section 2, a wide range of research challenges pertaining to COVID-19 have been explored and investigated via the analysis of relevant Tweets in scholarly works over the past few years. These include traveling [87], current trends [88], worries of the general public [88], the evaluation of events [89], opinions on mask-wearing [91], inquiries into influencer behaviors [92], detecting and tracking misinformation [93], studies of addiction trends [96], identifying loneliness [98], and the evaluation of impulse purchases [100]. Despite the extensive exploration of many research questions within this particular domain, the existing body of literature [87; 88; 89; 91; 92; 93; 94; 95; 96; 98; 100] has not specifically investigated the Twitter discourse pertaining to Long COVID. The research described in this study addresses this limitation found in previous studies in this field [87; 88; 89; 91; 92; 93; 96; 98; 100] by focusing on the analysis of Tweets related to Long COVID. * Despite the existence of some studies, such as those conducted by [PERSON] et al. [108] and [PERSON] et al.