Algorithms | Accuracy | Sensitivity Recall-TPR | Precision TPR-PPV | Specificity TNR | F-measure (F-score) | Balanced Accuracy | MCC |
Lexicon-based Approach | 0.707 | 0.829 | 0.796 | 0.362 | 0.812 | 0.596 | 0.161 |
Logistic Regression | 0.782 | 0.989 | 0.783 | 0.759 | 0.874 | 0,874 | 0.608 |
Naïve Bayes | 0.784 | 0.986 | 0.786 | 0.740 | 0.874 | 0.863 | 0.589 |
Decision Tree | 0.770 | 0.976 | 0.779 | 0.573 | 0.866 | 0.775 | 0.394 |
Multilayer Perceptron | 0.778 | 0.970 | 0.796 | 0.676 | 0.875 | 0.823 | 0.521 |
SVM Networks | 0.781 | 0.980 | 0.786 | 0.684 | 0.872 | 0.832 | 0.524 |
Random Forest | 0.780 | 0.925 | 0.865 | 0.563 | 0.865 | 0.744 | 0.401 |
XGBoost | 0.770 | 0.907 | 0.812 | 0.521 | 0.857 | 0.714 | 0.353 |
Convolutional Neural Network (CNN) | 0.772 | 0.921 | 0.808 | 0.535 | 0.861 | 0.728 | 0.366 |
Recurrent Neural Network (RNN) | 0.792 | 0.983 | 0.793 | 0.760 | 0.878 | 0.872 | 0.617 |