国家自然科学基金(61672126)
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Figure 1
(Color online) The architecture of DAGRU model
Figure 2
(Color online) DAGRU unit
| Sentence | Target entity | Sentiment polarity |
| (a) Great food but the service was dreadful | Food | Positive |
| Service | Negative | |
| (b) Except Patrick, all other actors don't play well | Patrick | Positive |
| Sentiment polarity | Laptop | Restaurant | ||
| Train | Test | Train | Test | |
| Positive | 994 | 341 | 2164 | 728 |
| Neutral | 464 | 169 | 637 | 196 |
| Negative | 870 | 128 | 807 | 196 |
| Total | 2328 | 638 | 3608 | 1120 |
| Parameter | Value |
| BiGRU hidden units | 64 |
| DAGRU hidden units | 128 |
| Dropout | 0.5 |
| Recurrent dropout | 0.5 |
| DAGRU layers $K$ | 5 |
| Batch size | 64 |
| Learning rate | 0.001 |
| Layers | Laptop (%) | Restaurant (%) | ||
| Joint encoding | Individual encoding | Joint encoding | Individual encoding | |
| 0 | 74.92 | 56.27 | 80.09 | 67.50 |
| 1 | 75.70 | 72.57 | 80.80 | 80.18 |
| 2 | 76.02 | 72.88 | 81.07 | 80.18 |
| 3 | 75.86 | 72.88 | 81.16 | 80.80 |
| 4 | $\boldsymbol{76.49}$ | 73.04 | 81.25 | $\boldsymbol{81.61}$ |
| 5 | 76.33 | $\boldsymbol{73.51}$ | $\boldsymbol{81.96}$ | 81.07 |
a) The blod number represents the highest result.
| Model | Laptop (%) | Restaurant (%) |
| Majority | 53.45 | 65.00 |
| Simple-SVM | 66.97 | 73.22 |
| Feature-enhauced SVM (Kiritchenko et al.) | 72.10 | 80.89 |
| TD-LSTM (Tang et al.) | 68.13 | 75.63 |
| AE-LSTM (Wang et al.) | 68.90 | 76.60 |
| ATAE-LSTM (Wang et al.) | 68.70 | 77.20 |
| MemNet (Tang et al.) | 70.33 | 79.98 |
| IAN (Ma et al.) | 72.10 | 78.60 |
| RAM (Chen et al.) | 74.49 | 80.23 |
| AOA-LSTM (Huang et al.) | 74.50 | 81.20 |
| LCR-Rot (Zheng and Xia) | 75.24 | 81.34 |
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