## 3 Methodology
After obtaining the results from Algorithm A5 (refer to Appendix A), the VADER sentiment analysis approach was applied to the Tweets. The subject of Sentiment Analysis can be explored using various methods, including manual categorization, Linguistic Inquiry and Word Count (LIWC), Affective Norms for English Words (ANEW), the General Inquirer (GI), SentiWordNet, and machine learning-based approaches like Naive Bayes, Maximum Entropy, and Support Vector Machine (SVM). In prior works in the field of Natural Language Processing [121; 122; 123; 124; 125] multiple approaches for sentiment analysis have been applied to the context of the sentiment analysis. Figure 1: A flowchart that represents the working of different algorithms to obtain the master dataset for analysis. been proposed, developed, and applied to analyze Tweets. However, the specific approach chosen for this study was VADER, which stands for Valence Aware Dictionary for Sentiment Reasoning [127]. The decision to opt for VADER as the sentiment analysis method is based on multiple factors. First and foremost, VADER has demonstrated exceptional performance, surpassing human labeling in terms of accuracy and effectiveness. Furthermore, VADER has been proven to overcome the limitations faced by other similar sentiment analysis techniques. The following provides a comprehensive overview of the distinct characteristics and features of VADER. 1. VADER sets itself apart from LIWC by displaying heightened sensitivity to sentiment expressions that commonly appear in the analysis of social media posts. 2. The General Inquirer lacks the inclusion of sentiment-relevant lexical elements frequently encountered in social communication. However, VADER effectively addresses this issue. 3. The ANEW lexicon exhibits reduced responsiveness to lexical elements typically associated with sentiment in social media content. This is not a limitation of VADER. 4. The SentiWordNet lexicon contains a significant amount of noise since a notable proportion of its synsets lack either positive or negative polarity. However, this does not represent a constraint or drawback of VADER. 5. The Naive Bayes classifier relies on the assumption of feature independence, which is a simplistic assumption. Nonetheless, VADER's more nuanced approach overcomes this weakness. 6. The Maximum Entropy technique incorporates information entropy by assigning feature weightings without assuming conditional independence between features. 7. Both machine learning classifiers and verified sentiment lexicons face the challenge of requiring a substantial amount of training data. Additionally, machine learning models depend on the training set to accurately represent a wide range of characteristics. The VADER approach distinguishes itself through its concise rule-based framework, enabling the creation of a specialized sentiment analysis engine tailored specifically for language found on social media platforms. The system demonstrates remarkable adaptability, capable of adjusting to different domains without the need for specific training data. Instead, it relies on a flexible, valence-based sentiment dictionary that has been validated by humans to serve as a reliable standard. The VADER system is renowned for its high efficiency, and it can also analyze streaming data. The VADER approach was applied to every Tweet to classify it as positive, negative, or neutral. Thereafter, the hashtag-specific sentiment analysis was performed for each of the top 10 hashtags, and the trends in the Tweets (positive, negative, and neutral) were analyzed. Algorithm A6 (refer to Appendix A) shows the pseudocode of the program that was written in Python 3.11.5 to compute the number of Tweets per sentiment per hashtag (top 10 hashtags). As can be seen in Algorithm A5 (refer to Appendix A), it called the algorithm for Data Preprocessing (Algorithm A7).