Table 10. Results of machine learning classifiers using BoW features.
Model | Accuracy | Class | Precision | Recall | F1 |
---|---|---|---|---|---|
SVM | 0.87 | −1 | 0.92 | 0.91 | 0.91 |
0 | 0.76 | 0.90 | 0.83 | ||
1 | 0.91 | 0.75 | 0.82 | ||
LR | 0.85 | −1 | 0.91 | 0.90 | 0.91 |
0 | 0.75 | 0.88 | 0.81 | ||
1 | 0.86 | 0.72 | 0.78 | ||
GNB | 0.53 | −1 | 0.67 | 0.46 | 0.54 |
0 | 0.66 | 0.53 | 0.59 | ||
1 | 0.38 | 0.69 | 0.49 | ||
ETC | 0.85 | −1 | 0.92 | 0.90 | 0.91 |
0 | 0.75 | 0.93 | 0.83 | ||
1 | 0.88 | 0.70 | 0.78 | ||
GBM | 0.88 | −1 | 0.94 | 0.90 | 0.92 |
0 | 0.79 | 0.94 | 0.86 | ||
1 | 0.88 | 0.77 | 0.82 | ||
ADA | 0.78 | −1 | 0.88 | 0.85 | 0.87 |
0 | 0.75 | 0.84 | 0.79 | ||
1 | 0.64 | 0.60 | 0.62 |