Research Article
Sentiment Prediction of Textual Data Using Hybrid ConvBidirectional-LSTM Model
Table 5
Accuracy comparison of the proposed model with the existing state-of-the-art models on the US airline dataset.
| Authors | Model | Accuracy (%) |
| Wen and Li [13] | ARC | 83.10 | RC | 83.20 | M_ARC | 83.30 |
| Dang et al. [10] | TF-IDF-CNN | 68.79 | Word embedding-CNN | 82.36 | TF-IDF-RNN | 61.74 | Word embedding-RNN | 83.76 |
| Umer et al. [12] | CNN-LSTM | 82.00 | Jain et al. [11] | LR | 88.20 | NB | 87.10 | DT | 78.10 | SVM | 80.20 | CNN | 87.10 | LSTM | 88.20 | CNN-LSTM | 91.30 |
| Basiri et al.[9] | ABCDM | 92.75 | Proposed model | GloVe-CNN-LSTM | 92.79 | GloVe-HeBiLSTM | 92.47 | ConvBidirectional-LSTM | 93.25 |
|
|