## 3 Methodology
### Theoretical Overview of Sentiment Analysis and Technical Overview of RapidMiner  
Sentiment Analysis, often referred to as Opinion Mining, represents the computational exploration of individuals' sentiments, viewpoints, and emotional expressions regarding a given subject. This subject can encompass various entities, such as individuals, events, or topics [120]. The terms Sentiment Analysis (SA) and Opinion Mining (OM) are commonly used interchangeably, denoting a shared essence. Nevertheless, some scholars have posited subtle distinctions between OM and SA [121; 122]. Opinion Mining, in its essence, involves the extraction and examination of people's opinions pertaining to a specific entity. In contrast, Sentiment Analysis seeks to identify the sentiment embedded within a text and subsequently analyze it. Consequently, SA endeavors to unearth opinions, discern the sentiments they convey, and classify these sentiments based on their polarity. This classification process can be envisioned as a three-tiered hierarchy comprising document-level, sentence-level, and aspect-level SA. At the document level, the primary objective is to categorize an entire opinion document as either expressing a positive or negative sentiment. Here, the document functions as the fundamental information unit typically focused on a single overarching topic or subject. In sentence-level SA, the aim is to classify the sentiment within each individual sentence. The initial step involves distinguishing between subjective and objective sentences. Subsequently, for subjective sentences, sentence-level SA ascertains whether they convey positive or negative opinions [123]. It is worth noting that [PERSON] et al. [124] highlighted that sentiment expressions may not always possess a subjective nature. However, the distinction between document and sentence-level classifications is not fundamentally profound since sentences can be regarded as concise documents [125]. While document and sentence-level classifications offer valuable insights, they often fall short of providing the granular details necessary for evaluating opinions on various facets of the entity. To obtain this comprehensive understanding, aspect-level SA comes into play. This level of analysis endeavors to categorize sentiments with respect to specific aspects or attributes associated with entities. The initial step involves the identification of these entities and their respective facets. Importantly, opinion-holders can articulate diverse sentiments concerning distinct aspects of the same entity. In essence, SA or OM encompasses a multifaceted process that spans various levels of analysis, from overarching documents to individual sentences and, ultimately, the nuanced evaluation of specific aspects related to entities. This comprehensive approach to sentiment analysis is invaluable in unveiling the intricate tapestry of opinions and emotions expressed in text data, enabling a deeper understanding of public sentiment in various contexts [126]. The examination of emotions can encompass a range of techniques, such as human annotation, 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). However, the methodology used in this study included the utilization of VADER, an acronym for Valence Aware Dictionary for Sentiment Reasoning [127]. The selection of VADER as the sentiment analysis methodology is influenced by many considerations. First of all, VADER demonstrates outstanding efficiency, surpassing manual annotation with respect to both precision and efficacy. Furthermore, previous research has indicated that VADER effectively addresses the limitations encountered by other sentiment analysis approaches. The following presents an overview of some of the unique features of VADER, as well as drawbacks in alternate sentiment analysis approaches, which are not present in VADER:  
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