## 3 Materials and Methods
### Methodology
#### 3.2.8 Evaluation Measures
\begin{table}
\begin{tabular}{l p{284.5 pt}} \hline \hline
**Models** & **Description** \\ \hline SVM & SVM is one of the most widely used models for sentiment analysis [43]. It performs classification by locating the hyper-plane that is the best match for differentiating the classes. SVM is a linear model which is used with kernel sigmoid and a c = 3.0 parameter (see Table 9). \\ \hline LR & LR is a supervised machine learning algorithm used to determine the probabilities of output variable [44]. It performs well when the nature of the output or dependent variable is binary, but it can also be good for multi-class data classification. It used the logistic function to categorize the data. \\ \hline DT & DT collects data in the form of a tree, which may alternatively be expressed as a collection of discrete rules [45]. Decision trees can handle big data well. The DT algorithm works to split the record according to the attribute selection measures technique and select the best set of attributes. \\ \hline RF & RF is a supervised learning algorithm. It can be used for both classification and regression. This algorithm is also the most flexible and easy to use [46]. The forest is made of trees, more trees in the forest, and the stronger they will be in prediction. RF makes random trees from randomly selected data samples, makes predictions from each tree, and votes for the best solution. \\ \hline SGD & SGD Classifier is a linear classifier that implements regularized linear models with a stochastic gradient descent as the cost function [47]. It supplies regularized linear models with SGD learning to build an estimator. The SGD classifier works well with large-scale datasets, and it is efficient and easy to implement the method. SGD is implemented using the sci-kit library. \\ \hline KNN & It is a supervised machine learning model used for classification of data [48]. It is a simple model which is easy to implement and interpret. KNN is also known as a lazy learner because it makes predictions based on the nearest neighbor by finding the distance. It performs well when the size of data is not too large. \\ \hline GNB & The GNB algorithm is a special kind of Naive Bayes algorithm that is unique. It is mostly used with continuous features. It is also expected that all of the characteristics have a Gaussian distribution or a normal distribution. Naive Bayes algorithms work on the basis of the Bayes theorem. If the data contain strongly correlated characteristics, the performance of Naive Bayes might suffer [49]. \\ \hline AdaBoost & AdaBoost is termed adaptive boosting, which is a supervised machine learning model used for the classification of data. It used a boosting mechanism to boost the classification accuracy. Adaboost used DT as a base leaner (“weak learner”) by default. The output of the learning algorithm is associated with weight, which is the end result of the density assessment [50]. \\ \hline ETC & ETC is a tree-based ensemble model used for the classification of data by training/fitting a large number of weak learners (randomized decision trees) on distinct samples of the dataset, ETC uses the majority voting criteria to enhance prediction accuracy [25]. It is an ensemble learning model that works similarly to RF. The only difference between ETC and RF is how the forest trees are constructed. \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Brief description of machine learning models used in this study. \begin{table}
\begin{tabular}{l p{284.5 pt}} \hline \hline
**Models** & **Hyper-Parameters** \\ \hline RF & n\_estimators = 300, max\_depth = 300 \\ \hline LR & solver = “saga”, multi\_class = “multinomial”, C = 3.0 \\ \hline SVM & Kernel = “linear”, C = 3.0 \\ \hline DT & max\_depth = 300, random state =2 \\ \hline KNN & n\_neighbour = 5 \\ \hline AdaBoost & n\_estimator = 50, learning\_rate =0.1 \\ \hline GNB & Default setting \\ \hline SGD & max iter = 200, tol = 1\(\times\) 10\({}^{-3}\) \\ \hline ETC & n\_estimators = 300, max\_depth = 300 \\ \hline \hline \end{tabular}
\end{table}