## 3 Methodology
The Data Preprocessing algorithm represented a program that was written to perform the necessary preprocessing of the Tweets prior to assigning a sentiment label (positive, negative, or neutral) to each Tweet. As Algorithm A7 (refer to Appendix A) has been called in multiple Algorithms that are presented in this paper, a step-by-step working of this Algorithm is shown in Figure 2.  
Thereafter, text processing, text analysis, and lexicon-based approaches were used to detect potentially sarcastic Tweets as well as Tweets that could contain news in the context of conversations about exoskeletons. For developing the methodology for sarcasm detection, prior works based on text processing were reviewed. It was observed that several works (for example, [128; 129; 130]) detected potentially sarcastic Tweets by either searching for \"sarcasm\" or \"sarcastic\" present in the form of words or hashtags in Tweets as sarcasm appears to be a commonly recognized concept by many Twitter users, who explicitly mark their sarcastic messages by using hashtags [131]. In addition to these approaches, another study in this field [132] was reviewed to understand lexical-based approaches for detecting sarcasm in Tweets. So, in this work, a combination of keyword-based, hashtag-based,and lexical analysis-based methodologies was utilized for detecting potentially sarcastic Tweets. These included hashtags or keywords such as \"sarcasm\", \"sarcastic\", \"irony\", and \"cynicism\"; interjections such as \"gee\" and \"gosh\"; lexical expressions such as \"not sure if you know this\"; formulaic expressions such as \"thanks a lot\", and \"good job\"; foreign terms such as \"au contraire\"; rhetorical statements such as \"tell us what you really think\"; and specific combinations of keywords such as \"perfect just perfect\". The approach in this context also accounted for different character case (upper case or lower case) combinations of these criteria to track potentially sarcastic Tweets. The pseudocode of the program that was written in Python 3.11.5 to detect potentially sarcastic Tweets from the dataset is shown in Algorithm A8 (refer to Appendix A). As can be seen from Algorithm A8 (refer to Appendix A), the output of this program produced a set of Tweets in a.CSV that were potentially sarcastic Tweets. Thereafter, a similar approach was used to detect Tweets that contained news. A review of prior works [133; 134] related to the detection of Tweets showed that researchers in this field have tracked the presence of \"news\" in hashtag form or in keyword form in Tweets to detect news communicated in different Tweets. So, the methodology in this work involved searching for \"news\" in hashtag form or in keyword form in the Tweets present in this dataset. The methodology also accounted for different character case (upper case or lower case) combinations in the keyword as well as in the hashtag. Algorithm A9 (refer to Appendix A) represents the pseudocode of the program that was written in Python 3.11.5 to detect Tweets that contained news. As can be seen from Algorithm A9, the output of this program produced a set of Tweets in a.CSV that contained news about exoskeletons. Thereafter, Algorithms A2 and A3 were run on the master dataset (shown in Figure 1), the.CSV file containing sentiment labels for each  
Figure 2: A workflow diagram representing the working of Algorithm A7 (refer to Appendix A). Tweet (one of the outputs of Algorithm A6), the.CSV file representing potentially sarcastic Tweets (output of Algorithm A8), and the.CSV file representing Tweets that contained news (output of Algorithm A9). The objective of running these algorithms on these Tweets was to compare the positive Tweets, negative Tweets, neutral Tweets, possibly sarcastic Tweets, and Tweets that contained news in terms of the mean length of the Tweets per month, the median length of the Tweets per month, the average number of hashtags used per month, and the average number of user mentions used per month, to interpret the underlying trends of the same.