## 3 Methodology
By using the concept of binning, 24 bins representing the 24 h in a day were created, and each Tweet was assigned a bin by extracting only the time information from this attribute. The pseudocode of the program that was written in Python 3.11.5 is shown in Algorithm A1 (refer to Appendix A). Using a similar approach as shown in Algorithm A1, the number of Tweets posted per month per year between May 2017 and May 2023 was also extracted to analyze the Tweeting patterns about exoskeletons in the last six years. Thereafter, different characteristics associated with these Tweets were computed. These characteristics included the mean value of the total number of characters used per hour, the median value of the total number of characters used per hour, the number of hashtags used per hour, and the number of user mentions included in the Tweets per hour. After calculating these characteristics, these features were assigned to the bins. As a result of this assignment, for each hour of all the Tweets posted, the mean value of the total number of characters used, the median value of the total number of characters used, the number of hashtags used, and the number of user mentions present were compiled. Algorithm A2 (refer to Appendix A) shows the pseudocode of the program that was written in Python 3.11.5 to compute the mean and median values of the Tweets per hour. The pseudocode of the program to calculate hashtags and user mentions per hour is shown in Algorithm A3 (refer to Appendix A). The flowchart shown in Figure 1 summarizes the working of the above-mentioned algorithms. After obtaining this master dataset for analysis, as shown in Figure 1, the correlations between these characteristics and the number of Tweets per hour were evaluated to deduce if those characteristics were statistically significant (\(p<0.05\)). After analyzing these correlations (using [PERSON]'s correlation coefficient), it was observed that all these characteristics, i.e., the mean value of characters used in the Tweets per hour, the median value of characters used in the Tweets per hour, the number of hashtags used per hour, and the number of user mentions used per hour, had statistically significant relationships with the number of Tweets per hour (results are discussed in detail in Section 4). Thereafter, a multiple linear regression model was developed where the mean value of characters used in the Tweets per hour, the median value of characters used in the Tweets per hour, the number of hashtags used per hour, and the number of user mentions used per hour were considered as the independent variables, and the number of Tweets per hour was considered as the dependent variable. Algorithm A4 (refer to Appendix A) shows the pseudocode of the program that was written in Python 3.11.5 to determine these correlations as well as to develop the multiple linear regression model. Thereafter, the focus of the investigation shifted towards hashtag-specific sentiment analysis. This was considered relevant for investigation primarily because prior works that focused on the investigation of Tweets for sentiment analysis (Section 2) did not determine a list of popular hashtags and their associated sentiments. However, determining a list of popular hashtags and their associated sentiments has been popular in the area of Natural Language Processing, as can be seen from multiple recent works in this field that focused on hashtag-specific sentiment analysis of Tweets about COVID-19 [118], politics [119], and movies [120], just to name a few. To perform the same, at first, the list of the top 10 hashtags (in terms of the number of Tweets posted) was determined. Then, the number of Tweets per hashtag (out of these top 10 hashtags) per month between May 2017 and May 2023 was computed to understand the trends of the same. Algorithm A5 (refer to Appendix A) shows the pseudocode of the program that was written in Python 3.11.5 to determine the top 10 hashtags.