## 4 Results
Figure 31: A graphical representation to compare the positive Tweets, negative Tweets, possibly sarcastic Tweets, and Tweets that contained news in terms of the average number of user mentions present in the Tweets on a monthly basis. Figure 30: A graphical representation to compare the positive Tweets, negative Tweets, possibly sarcastic Tweets, and Tweets that contained news in terms of the average number of hashtags present in the Tweets on a monthly basis. The analysis of Tweets that contained news also revealed that news articles were disseminated by embedding the URLs from different news sources in the Tweets. In terms of embedded URLs present in the Tweets, the highest number of times a URL was observed to be embedded in this dataset was 268. This was the URL of a news article regarding a type of exoskeleton [137]. Similarly, another URL that was embedded the second highest number of times (201 times) was also a news article about a type of exoskeleton [138]. Thereafter, as discussed in Section 3, a fine-grain analysis of the sentiments was also performed to detect different sentiment classes such as anger, disgust, fear, joy, neutral, sadness, and surprise (pseudocode presented in Algorithm A10). As Figure 17 reports, 33.1% of the Tweets were neutral Tweets, so the neutral Tweets were removed prior to the data analysis to understand the distribution of sentiment classes such as anger, disgust, fear, joy, sadness, and surprise in the remainder of the Tweets. The results of this analysis are shown in Figure 32. As can be seen from this figure, the sentiment of surprise was the most common emotion. It was followed by joy, disgust, sadness, fear, and anger, respectively. Next, a comparison of the work of this paper with prior works in this field in terms of the focus areas is presented in Table 4. As can be seen from Table 4, the work presented in this paper is the first paper in this area of research that focuses on multimodal forms of content analysis, text analysis, sentiment analysis, fine-grain sentiment analysis, and hashtag-specific sentiment analysis in the context of Tweets about exoskeletons. Furthermore, this work also presents a multiple linear regression model to predict Tweets posted about exoskeletons on a monthly basis in terms of specific characteristics of the Tweets. This study has a few limitations. First, the 10 most popular hashtags were identified, and trends of sentiment associated with those hashtags were analyzed in this study. However, the conversations on Twitter related to exoskeletons are diverse and cover multiple topics, including recent developments. As a result, over the course of time, new hashtags may emerge related to such recent developments and may become more popular than the  
Figure 15:0 A representation of the number of Tweets for each of the fine-grain sentiment classes—anger, disgust, fear, joy, sadness, and surprise. hashtags identified and analyzed in this study. Second, Twitter users do not represent the entire global population. So, the sentiments regarding exoskeletons identified and analyzed in this study reflect the opinions of a subset of the global population towards exoskeletons. Finally, the Tweets analyzed in this research were still accessible on Twitter at the time of data analysis. However, Twitter provides users with the option to remove their Tweets and deactivate their accounts. Moreover, in accordance with Twitter's guidelines on inactive accounts [139], Twitter reserves the right to permanently delete accounts that have been inactive for an extended period of time, hence leading to the deletion of all Tweets posted from such accounts. If this study were to be replicated in the future, it is possible that the findings could exhibit some variation as compared to the results presented in this paper if any of the examined Tweets were removed as a result of users deleting them, users deleting their accounts, or Twitter permanently deleting one or more of the accounts from which the analyzed Tweets were posted.