## 4 Results and Discussion
### Results Using TF-IDF Features and the SMOTE Technique
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **Accuracy** & **Class** & **Precision** & **Recall** & **F1 Score** \\ \hline \multirow{4}{*}{LR} & \multirow{4}{*}{0.92} & 0 & 0.88 & 0.99 & 0.93 \\ \cline{3-5}  & & 1 & 0.96 & 0.93 & 0.94 \\ \cline{3-5}  & & \(-1\) & 0.97 & 0.60 & 0.74 \\ \cline{3-5}  & & Macro avg & 0.94 & 0.84 & 0.87 \\ \hline \multirow{4}{*}{RF} & \multirow{4}{*}{0.94} & 0 & 0.90 & 1.00 & 0.95 \\ \cline{3-5}  & & 1 & 0.98 & 0.92 & 0.95 \\ \cline{3-5}  & & \(-1\) & 0.96 & 0.76 & 0.85 \\ \cline{3-5}  & & Macro avg & 0.95 & 0.89 & 0.92 \\ \hline \multirow{4}{*}{DT} & \multirow{4}{*}{0.93} & 0 & 0.94 & 0.94 & 0.94 \\ \cline{3-5}  & & 1 & 0.95 & 0.95 & 0.95 \\ \cline{3-5}  & & \(-1\) & 0.82 & 0.83 & 0.82 \\ \cline{3-5}  & & Macro avg & 0.90 & 0.91 & 0.90 \\ \hline \multirow{4}{*}{KNN} & \multirow{4}{*}{0.60} & 0 & 0.98 & 0.54 & 0.70 \\ \cline{3-5}  & & 1 & 0.26 & 0.95 & 0.70 \\ \cline{3-5}  & & \(-1\) & 0.44 & 0.70 & 0.57 \\ \cline{3-5}  & & Macro avg & 0.56 & 0.75 & 0.58 \\ \hline \multirow{4}{*}{SVM} & \multirow{4}{*}{0.95} & 0 & 0.91 & 0.99 & 0.95 \\ \cline{3-5}  & & 1 & 0.98 & 0.93 & 0.95 \\ \cline{3-5}  & & \(-1\) & 0.94 & 0.78 & 0.85 \\ \cline{3-5}  & & Macro avg & 0.94 & 0.90 & 0.92 \\ \hline \multirow{4}{*}{AdaBoost} & \multirow{4}{*}{0.77} & 0 & 0.68 & 1.00 & 0.81 \\ \cline{3-5}  & & 1 & 0.97 & 0.59 & 0.73 \\ \cline{3-5}  & & \(-1\) & 0.87 & 0.62 & 0.72 \\ \cline{3-5}  & & Macro avg & 0.84 & 0.74 & 0.76 \\ \hline \multirow{4}{*}{GNB} & \multirow{4}{*}{0.68} & 0 & 0.91 & 0.58 & 0.71 \\ \cline{3-5}  & & 1 & 0.87 & 0.75 & 0.81 \\ \cline{3-5}  & & \(-1\) & 0.24 & 0.78 & 0.37 \\ \cline{3-5}  & & Macro avg & 0.68 & 0.70 & 0.63 \\ \hline \multirow{4}{*}{ETC} & \multirow{4}{*}{0.91} & 0 & 0.92 & 0.93 & 0.93 \\ \cline{3-5}  & & 1 & 0.93 & 0.92 & 0.93 \\ \cline{3-5}  & & \(-1\) & 0.81 & 0.81 & 0.81 \\ \cline{3-5}  & & Macro avg & 0.89 & 0.89 & 0.89 \\ \hline \multirow{4}{*}{SGD} & \multirow{4}{*}{0.94} & 0 & 0.89 & 0.99 & 0.94 \\ \cline{3-5}  & & 1 & 0.98 & 0.93 & 0.95 \\ \cline{3-5}  & & \(-1\) & 0.98 & 0.73 & 0.84 \\ \cline{3-5}  & & Macro avg & 0.95 & 0.88 & 0.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Results using TF-IDF features and the SMOTE technique. On average, the performance of all models has been improved substantially when TF-IDF features are used from the SMOTE balanced dataset as compared to the imbalanced dataset. In addition to an approximately equal number of samples for each class, balancing the dataset increases the feature set as well due to generating artificial data to make the dataset balanced. This data generation creates more features for learning models, and linear learner such as SVM is the best performer on large feature sets. Consequently, models get good accuracy when the SMOTE technique is used for generating synthetic samples of the minority class. Comparative analysis between results of BoW and TF-IDF indicates that there is no significant difference in the performance of machine learning models when models are trained using BoW or TF-IDF features on the original dataset that contains a different number of samples for three classes. The similarity in models performance can be seen in Figure 3, which indicates that the difference in the performance of RF and AdaBoost is marginal while LR, DT, KNN, SVM, GNB, ETC, and SGD are the same. Similarly, Figure 4 shows comparative accuracy of the models using BoW and TF-IDF features from SMOTE balanced data. Although the performance is improved substantially, the difference in the performance is little between BoW and TF-IDF features except for GNB, where accuracy with BoW and TF-IDF is 0.78 and 0.68, respectively. Figure 4: Models’ performance comparison on BoW and TF-IDF features when we used the SMOTE technique. Figure 3: Models’ performance comparison using BoW and TF-IDF on the original imbalanced dataset. Table 14 summarizes the average accuracy for positive, negative, and neutral classes for the machine learning models with BoW and TF-IDF for the original and balanced datasets.