Table 4 Results comparison with existing works
S/N | References | Proposed methodology | Accuracy (%) | Precision (%) | Recall (%) | AUC values (%) |
|---|---|---|---|---|---|---|
1 | Anitha and Metilda [34] | SVM | 70.66 | 71.45 | 71.33 | – |
|  |  | NB | 66.97 | 67.31 | 67.33 | – |
|  |  | RNN | 69.34 | 70.04 | 70.05 | – |
2 | Basiri et al. [35] | CNN | 81.60 | – | – | – |
|  |  | BiGRU | 79.7 | – | – | – |
3 | Qi and Shabrina [21] | Random forest | 45.00 | 53.00 | 47.00 | – |
|  |  | MultinomialNB | 62.00 | 61.33 | 58.00 | – |
|  |  | SVC | 71.00 | 69.00 | 69.67 | – |
4 | Parveen et al. [6] | *GARN | 96.12 | 93.90 | 94.34 | – |
|  |  | BiLSTM | 94.59 | 91.99 | 92.27 | – |
|  |  | **BiGRU | 95.79 | 93.59 | 94.06 | – |
5 | Mahadevaswamy Mohamad Sham and Mohamed [36] | BiLSTM | 91.40 | – | – | – |
6 | Minaee et al. [37] | LSTM and CNN | 90.00 | – | – | – |
7 | Senthil and Malarvizhi [38] | LSTM-CNN | 98.60 | 73.00 | 83.00 | 98.60 |
|  |  | LSTM | 89.50 | – | – | 89.5 |
|  |  | SAMF-BiLSTM | 83.30 | – | – | – |
| Â | Proposed method | BiLSTM | 78.29% | 78.26% | 78.27% | 86.55 |