## 3 Methodology
### Theoretical Overview of Sentiment Analysis and Technical Overview of RapidMiner
VADER differentiates itself from LIWC by exhibiting enhanced sensitivity towards sentiment patterns that are often seen in the analysis of texts from social media. 3. The General Inquirer has a limitation in its incorporation of sentiment-relevant linguistic components frequently observed in conversations on social media. 4. The ANEW lexicon exhibits a reduced degree of reactivity regarding the linguistic components often linked to emotion in social media posts. 5. The SentiWordNet lexicon exhibits a significant level of noise, as a noteworthy fraction of its synsets lack clear opposite polarity. 6. The Naive Bayes classifier is predicated on the premise of feature independence, which might be considered a simplistic premise. VADER's more nuanced strategy effectively addresses this limitation. 7. The Maximum Entropy approach integrates the concept of information entropy by providing feature weightings without making the assumption of conditional independence between features. 8. Both machine learning classifiers and validated sentiment lexicons face the same obstacle of requiring a significant quantity of data for training. Furthermore, the efficacy of machine learning algorithms is contingent upon the training set's ability to correctly capture a diverse array of properties. The VADER technique is characterized by the implementation of a succinct rule-based architecture, enabling the creation of a specialized sentiment analysis algorithm tailored to the vocabulary often seen on social media sites. The framework exhibits remarkable adaptability in its ability to adjust to several contexts without requiring domain-specific learning information. Instead, it utilizes a versatile sentiment lexicon determined by valence, which has undergone rigorous evaluation by human experts to establish its credibility as a consistent benchmark. The VADER approach is renowned for its exceptional efficacy due to its ability to perform the real-time evaluation of continuous information streams. In addition, the system is capable of maintaining its computational efficiency, as evidenced by its time complexity of O(N). Furthermore, it is important to highlight that VADER is easily accessible to everyone without any prerequisites for subscription or financial transactions. Finally, VADER possesses the ability to evaluate the intensity of sentiment expressed in texts, in addition to categorizing it into positive, negative, or neutral polarities. In order to set up the system design for sentiment analysis in this study, RapidMiner was utilized [128]. RapidMiner, initially referred to as Yet Another Learning Environment (YALE), is a highly adaptable data science software framework that facilitates the development, execution, and utilization of a broad spectrum of models and algorithms from different disciplines. RapidMiner Studio adopts an open-core paradigm with a comprehensive Graphical User Interface (GUI) designed to allow users to create a variety of applications and processes. Additionally, it facilitates features to support the design and implementation of algorithms. In RapidMiner, designated actions or functions are referred to as \"operators\". These operators may be organized in a logical sequence, either sequentially in a hierarchy or in some combination of both, to form a \"process\" aimed at accomplishing a certain purpose or task. RapidMiner facilitates the design and implementation of these \"processes\" by providing a wide range of already assembled \"operators\" that may be readily used. Moreover, there exists a distinct category of \"operators\" that may be used to alter the fundamental attributes of other \"operators\", introducing an additional level of adaptability and personalization to the platform.