## 3 Research Methodology
### Polarity Determination  
The polarity of a sentence determines its orientation in a positive, negative, or neutral manner. It is commonly used in the reviews of blogs, microblogs, or forums. The type and amount of information are primarily unstructured and require high-level processing and intelligent analytical techniques. There are four approaches to this task: semantic, machine learning, deep learning, and hybrid. The methods used in this study were as follows. Lexicon-based approach (LBA): LBA depends on the sentiment lexicon and uses a bank of pre-coded words to determine the semantic orientation of the text [74]. This method includes two approaches: first, a dictionary is created with some words as a base; then, the antonyms and synonyms of those words are searched and new words are added to improve it. The corpus-based approach determines sentiment orientation from a context-specific list of words. This approach comprises two sub-approaches: semantic and statistical [75]. LBA is widely used in conventional texts, such as reviews, forums, and blogs. However, it is less likely to be used for big data extracted from SM websites [76]. LBS has the disadvantage of offering unacceptable accuracy rates because the polarity shown in the lexicon may distort polarity. This situation can be improved by generating context-specific lexicons [77; 78]. LBA does not require training data (as the supervised machine-learning method does) and has been used in previous studies to identify sentiments, particularly for Twitter and Sina Weibo. The valence-aware dictionary for sentiment reasoning (VADER): This is a rule-based lexical sentiment analysis tool that adapts to text analysis to generate a score (polarity). It uses modified scores for lexical attributes present in a sentence and determines the overallaverage sentiment score. The particular design of this method is suitable for the analysis of social networks; however, it can analyze texts of any length [79]. Moreover, it generates an intensity to determine whether a sentiment is positive or negative. VADER has better precision than other types of linguistic analyses [80]. This is one reason why it is an ideal tool for our research. Machine Learning Approach (MLA): Machine learning (ML) has been developed in recent years and offers new ways to address real-world challenges. The MLA uses linguistic methods and applies common ML algorithms. These are classified into supervised and unsupervised approaches. Supervised learning methods aim to determine the desired output variable for a given input variable. The algorithm is identified using a learning method through mapping. In an unsupervised manner, there is no adequate input data. It aims to model the underlying structure or distribution to learn more about the data and identify the output. Supervised learning algorithms require training data for sentiment classifier learning, are domain-dependent, and require retraining with the arrival of new data. The learning stops when the algorithm reaches an acceptable level of performance. In MLA, classifiers are primarily trained using a feature set composed of n-grams, and there is a need for a large number of known examples to train them. Studies have shown that these types of classifiers perform better than lexicon-based ones. This situation is limited to a single domain and is non-transferable between applications. This limitation is one reason researchers have introduced \"hybrid approaches\" [81]. According to the literature, support vector machine (SVM) and Naive Bayes (NB) are the most commonly used classifiers [55; 82; 83; 84]. The SVM is a classification algorithm that aims to define the most significant margin of separation of the hyperplane of classes [37]. This algorithm is efficient and suitable for use with textual data. According to [PERSON] et al. [85], there are three main advantages of this classifier. First, it is robust in high-dimensional spaces and when there is a sparse set of samples. Second, any type of feature is relevant.