## 4 Results and Discussion
### Results Using TF-IDF Features and the SMOTE Technique  
Experiments are performed using TF-IDF on the balanced dataset as well as using SMOTE for balancing the minority class samples. Table 13 shows results in terms of accuracy, precision, recall, and F1 score for all the machine learning classifiers used in this study. Results indicate that models' performance has been improved significantly as compared to the models' performance on imbalanced data when TF-IDF features are used for training the models. Analogous to the performance using BoW with SMOTE, SVM shows superior performance with a 0.95 accuracy and significant precision, recall, and F1 scores. The difference in accuracy and other metrics is small, which indicates that the model has a good fit. The accuracy of RF and SGD is marginally lower than SVM with 0.94 accuracy each, followed by DT which obtains an accuracy of 0.93. \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}{*}{0.93} & 0 & 0.92 & 0.92 & 0.92 \\ \cline{3-5}  & & 1 & 0.97 & 0.93 & 0.95 \\ \cline{3-5}  & & \(-1\) & 0.90 & 0.95 & 0.92 \\ \cline{3-5}  & & Macro avg & 0.93 & 0.93 & 0.93 \\ \hline \multirow{4}{*}{RF} & \multirow{4}{*}{0.86} & 0 & 0.86 & 0.79 & 0.82 \\ \cline{3-5}  & & 1 & 0.96 & 0.83 & 0.89 \\ \cline{3-5}  & & \(-1\) & 0.78 & 0.95 & 0.86 \\ \cline{3-5}  & & Macro avg & 0.86 & 0.86 & 0.86 \\ \hline \multirow{4}{*}{DT} & \multirow{4}{*}{0.83} & 0 & 0.84 & 0.73 & 0.78 \\ \cline{3-5}  & & 1 & 0.92 & 0.83 & 0.87 \\ \cline{3-5}  & & \(-1\) & 0.76 & 0.95 & 0.84 \\ \cline{3-5}  & & Macro avg & 0.84 & 0.83 & 0.83 \\ \hline \multirow{4}{*}{KNN} & \multirow{4}{*}{0.52} & 0 & 0.30 & 0.87 & 0.45 \\ \cline{3-5}  & & 1 & 0.27 & 0.97 & 0.42 \\ \cline{3-5}  & & \(-1\) & 0.99 & 0.41 & 0.58 \\ \cline{3-5}  & & Macro avg & 0.52 & 0.75 & 0.48 \\ \hline \multirow{4}{*}{SVM} & \multirow{4}{*}{0.94} & 0 & 0.94 & 0.92 & 0.93 \\ \cline{3-5}  & & 1 & 0.98 & 0.94 & 0.99 \\ \cline{3-5}  & & \(-1\) & 0.89 & 0.95 & 0.92 \\ \cline{3-5}  & & Macro avg & 0.94 & 0.94 & 0.94 \\ \hline \multirow{4}{*}{AdaBoost} & \multirow{4}{*}{0.69} & 0 & 0.53 & 1.00 & 0.70 \\ \cline{3-5}  & & 1 & 0.94 & 0.74 & 0.83 \\ \cline{3-5}  & & \(-1\) & 0.98 & 0.33 & 0.49 \\ \cline{3-5}  & & Macro avg & 0.82 & 0.69 & 0.67 \\ \hline \multirow{4}{*}{GNB} & \multirow{4}{*}{0.78} & 0 & 0.91 & 0.63 & 0.74 \\ \cline{3-5}  & & 1 & 0.87 & 0.77 & 0.82 \\ \cline{3-5}  & & 1 & 0.87 & 0.77 & 0.82 \\ \cline{3-5}  & & \(-1\) & 0.65 & 0.93 & 0.77 \\ \cline{3-5}  & & Macro avg & 0.81 & 0.78 & 0.78 \\ \hline \multirow{4}{*}{ETC} & \multirow{4}{*}{0.80} & 0 & 0.79 & 0.72 & 0.75 \\ \cline{3-5}  & & 1 & 0.87 & 0.76 & 0.82 \\ \cline{3-5}  & & \(-1\) & 0.76 & 0.93 & 0.84 \\ \cline{3-5}  & & Macro avg & 0.81 & 0.80 & 0.80 \\ \hline \multirow{4}{*}{SGD} & \multirow{4}{*}{0.94} & 0 & 0.93 & 0.93 & 0.93 \\ \cline{3-5}  & & 1 & 0.97 & 0.94 & 0.96 \\ \cline{3-5}  & & \(-1\) & 0.91 & 0.95 & 0.93 \\ \cline{3-5}  & & Macro avg & 0.94 & 0.94 & 0.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Results using BoW features on the original dataset.