Table 7.
Classwise study of our proposed method using classification performance (%).
| CNN | Positive | Neutral | Negative | Overall | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| P | R | F | P | R | F | P | R | F | P | R | F | A | |
| CNNft | 69.4 | 74.6 | 71.8 | 51.8 | 22.4 | 31.1 | 69.5 | 76.8 | 72.8 | 63.5 | 57.9 | 58.5 | 68.1 |
| CNNds | 68.0 | 59.6 | 62.6 | 12.7 | 00.2 | 00.5 | 59.0 | 82.7 | 68.6 | 46.5 | 47.5 | 43.9 | 61.5 |
| CNNda | 64.1 | 57.6 | 60.5 | 43.9 | 04.5 | 08.2 | 57.4 | 78.6 | 66.3 | 55.1 | 46.9 | 45.0 | 59.5 |
| CNNc | 71.8 | 72.0 | 71.9 | 63.0 | 14.7 | 23.7 | 66.8 | 82.7 | 73.8 | 67.2 | 56.4 | 56.4 | 68.7 |
Note/ P, R, and F denote Precision, Recall, and F1-score for three classes (positive, negative, and neutral), respectively. Note that the hyperparameters used in our models are as follows: learning-rate: 1e − 05, batch-size: 32, epochs: 50, and optimizer: RMSProp. Boldface denotes the highest performance.