## 3 Methodology
### Overview of the System Architecture and Design  
To develop a \"process\" in RapidMiner for addressing this research problem, at first, a relevant dataset had to be identified. The dataset used for this work is a dataset of 1,244,051 Tweets about Long COVID, posted on Twitter between 25 May 2020 and 31 January 2023 [129]. This dataset contains a diverse range of topics represented in the Tweets about Long COVID, present in two different files. To develop the \"process\" (shown in Figure 1), these files were imported into RapidMiner and merged using the Append \"operator\". As the focus of this study is to analyze Tweets about Long COVID wherein individuals self-reported Long COVID, a text processing-based approach was applied to identify such Tweets. By reviewing similar works in this field that studied Tweets involving self-reporting of mental health problems [110], autism [111], dementia [112], depression [113], breast cancer [114], swine flu [115], flu [116], chronic stress [117], post-traumatic stress disorder [118], and dental issues [119], a Bag of Words was developed that contained specific phrases related to the self-reporting of Long COVID. These phrases were--\"I have long COVID\", \"I am experiencing long COVID\", \"I have been experiencing long COVID\", \"I am suffering from long COVID\", \"I am going through long covid\", \"I have been feeling symptoms of long covid\", \"I have had long covid\", \"I have been diagnosed with long covid\", \"I had long covid\", \"I am feeling symptoms of long covid\", \"I am experiencing long covid\", \"I felt symptoms of long covid\", \"I experienced long covid\", and \"I am diagnosed with long covid\". A data filter was used to apply the Bag of Words methodology to the Tweets, resulting in a set of 7348 Tweets in which individuals self-reported Long COVID. Subsequently, all these Tweets underwent data preprocessing. The procedure for data preprocessing included the following sequential steps:  
1. The elimination of non-alphabetic characters. 2. The elimination of URLs. 3. The elimination of hashtags. 4. The elimination of user mentions. 5. The identification of English words using the process of tokenization. 6. Stemming and Lemmatization. 7. The elimination of stop words. 8. The elimination of numerical values. 9. Addressing missing values. A number of functionalities were built in RapidMiner for the purpose of performing data preprocessing. Subsequently, the Extract Sentiment operator within the RapidMiner platform was tailored to utilize the VADER sentiment analysis methodology for the purpose of analyzing this corpus of 7348 Tweets. The VADER approach calculated a compound sentiment score ranging from +4 to \(-4\) for all these Tweets. A methodology following a set of specific rules was devised to deduce the number of positive, negative, and neutral Tweets. The operational mechanism of this rule-based approach included the analysis of the compound sentiment score assigned to every single Tweet. If the compound sentiment score was greater than zero, the Tweet was categorized as positive; conversely, if the compound sentiment score was less than zero, the Tweet was categorized as negative. In the event that the compound sentiment score was exactly 0, the Tweet was categorized as neutral. This technique consisted of various functions that operated sequentially. These functions were implemented as \"operators\" grouped as a \"sub-process\" within the primary \"process\", which was designed and implemented in RapidMiner for the purpose of performing sentiment analysis. Figure 1 depicts this primary \"process\". In Figure 1, \"Dataset-F1\" and \"Dataset-F2\" are used to refer to the two files in the dataset [129] that were imported into RapidMiner for system design and development. This \"process\", shown in Figure 1, comprises two sub-processes for data preprocessing and the rule-based identification of sentiment classes. The specific \"operators\" that comprised these sub-processes are shown in Figures 2 and 3, respectively.