## 6 Conclusions  
The popularity of social media platforms has been on an exponential rise in the last decade and a half, as social media platforms provide a seamless means for users to connect, communicate, and collaborate with each other. Out of different social media platforms, analysis of conversations on Twitter has been of significant interest to researchers from different disciplines. This can be inferred from the fact that in the last few years, there have been several works that focused on the analysis of Tweets about emerging technologies, matters of global interest, and topics of global concern such as ChatGPT, the Russia-Ukraine war, cryptocurrency markets, virtual assistants, abortions, loneliness, housing needs, fake news, religion, early detection of health-related problems, elections, education, pregnancy, food insufficiency, and virus outbreaks such as MPox, flu, H1N1, and COVID-19, just to name a few. Even though a wide range of topics and several emerging technologies have been investigated in recent works, there has not been any prior work in this field thus far that has focused on the analysis of Tweets about exoskeletons. The rapid advancement of exoskeleton technology is being propelled by its extensive range of applications. Some of these use cases involve assisting elderly individuals and those with disabilities in their daily tasks, increasing productivity and alleviating fatigue in military personnel, enhancing the quality of life for amputees and individuals with paralysis in different body parts, aiding firefighters in climbing and lifting heavy equipment, bolstering labor efficiency, and facilitating the transportation of bulky machinery in different industrial settings. As a result of these expanding use cases of exoskeletons, the general public has shared their views, opinions, and perspectives about exoskeletons on Twitter in the last few years on social media platforms, such as Twitter. The work presented in this paper aims to address this research gap as well as contribute towards advancing research in the area of exoskeleton technology by presenting several novel findings from a comprehensive analysis of about 150,000 Tweets about exoskeletons posted between May 2017 and May 2023. First, findings from a comprehensive content analysis and temporal analysis of these Tweets reveal the specific months when a significantly higher volume of Tweets was posted, and the time windows when the highest number of Tweets, the lowest number of Tweets, Tweets with the highest number of hashtags, and Tweets with the highest number of user mentions have been posted. Second, the paper shows that there are statistically significant correlations between the number of Tweets posted per hour and different characteristics of tweeting behavior, such as the number of characters (mean value) in the Tweets per hour, the number of characters (median value) in the Tweets per hour, the number of hashtagsused in the Tweets per hour, and the number of user mentions used in the Tweets per hour. Third, the paper presents a multiple linear regression model to predict the number of Tweets posted per hour in terms of these characteristics of tweeting behavior. The R\({}^{2}\) score of this model was observed to be 0.9540. Fourth, the paper reports that the 10 most popular hashtags were #exoskeleton, #robotics, #iot, #technology, #tech, #innovation, #ai, #sci, #construction, and #news. Fifth, an exploratory sentiment analysis of these Tweets was performed using VADER and the DistilRoBERTa-base library in Python. The findings show that 46.8% of the Tweets were positive, 33.1% of the Tweets were neutral, and 20.1% of the Tweets were neutral. The findings also show that in the Tweets that did not express a neutral sentiment, the sentiment of surprise was the most common emotion. It was followed by joy, disgust, sadness, fear, and anger, respectively. Furthermore, analysis of hashtag-specific sentiments revealed several novel insights associated with the tweeting behavior of the general public in this regard.