Filter | Stopwords filter | Attributes Selection | TP | FP | FN | Pre. | Recall | F1 | |
---|---|---|---|---|---|---|---|---|---|
SVM, Unigram | |||||||||
1. | No Filter | No | No – 703 | 110 | 16 | 32 | 0.87 | 0.77 | 0.82 |
2. | No Filter | No | Yes – 569 | 106 | 16 | 36 | 0.87 | 0.75 | 0.80 |
3. | No Filter | Yes | No – 764 | 99 | 15 | 43 | 0.86 | 0.70 | 0.77 |
4. | No Filter | Yes | Yes – 572 | 98 | 17 | 44 | 0.85 | 0.70 | 0.76 |
5. | Computer | No | No – 654 | 113 | 11 | 29 | 0.91 | 0.80 | 0.85 |
6. | Computer | No | Yes – 576 | 114 | 10 | 28 | 0.92 | 0.80 | 0.86 |
7. | Computer | Yes | No – 654 | 106 | 12 | 36 | 0.90 | 0.75 | 0.82 |
8. | Computer | Yes | Yes – 586 | 106 | 12 | 36 | 0.90 | 0.75 | 0.82 |
SVM, Trigram | |||||||||
9. | No Filter | No | No – 743 | 104 | 17 | 38 | 0.86 | 0.73 | 0.79 |
10. | No Filter | No | Yes – 639 | 104 | 16 | 38 | 0.87 | 0.73 | 0.79 |
11. | No Filter | Yes | No – 783 | 106 | 13 | 36 | 0.89 | 0.75 | 0.81 |
12. | No Filter | Yes | Yes – 648 | 105 | 13 | 37 | 0.89 | 0.74 | 0.81 |
13. | Computer | No | No – 720 | 107 | 12 | 35 | 0.90 | 0.75 | 0.82 |
14. | Computer | No | Yes – 602 | 104 | 13 | 38 | 0.89 | 0.73 | 0.80 |
15. | Computer | Yes | No – 758 | 102 | 11 | 40 | 0.90 | 0.72 | 0.80 |
16. | Computer | Yes | Yes – 648 | 105 | 13 | 38 | 0.89 | 0.74 | 0.81 |
SVM Applied on Accept file of our Approach | |||||||||
17. | Computer | NA | No – 136 | 61 | 10 | 81 | 0.86 | 0.43 | 0.57 |
18. | Computer | NA | Yes – 103 | 61 | 10 | 81 | 0.86 | 0.43 | 0.57 |
Results from Applying SVM Train Model on Test | |||||||||
19. | Computer | NA | No – 654 | 162 | 8 | 92 | 0.95 | 0.64 | 0.76 |
20. | Computer | NA | Yes – 576 | 158 | 9 | 96 | 0.95 | 0.62 | 0.75 |