Research Article
Interactive Dual Attention Network for Text Sentiment Classification
Table 3
Performance comparison with baseline methods.
| Approach | ChnSentiCorp | NLPCC-CN | NLPCC-EN | MR | Accuracy | Macro-F1 | Accuracy | Macro-F1 | Accuracy | Macro-F1 | Accuracy | Macro-F1 |
| SVM | 0.8618 | 0.8528 | 0.7479 | 0.7441 | 0.8226 | 0.8143 | 0.7914 | 0.7852 | LSTM | 0.8681 | 0.8570 | 0.7572 | 0.7557 | 0.8381 | 0.8379 | 0.7844 | 0.7705 | BiLSTM | 0.8831 | 0.8693 | 0.7603 | 0.7573 | 0.8488 | 0.8477 | 0.7941 | 0.7877 | ATT-BiLSTM | 0.8945 | 0.8892 | 0.7665 | 0.7585 | 0.8503 | 0.8491 | 0.7952 | 0.7909 | H-RNN-CNN | 0.8940 | 0.9030 | 0.7550 | 0.7790 | — | — | 0.8190 | — | CRNN | 0.9108 | 0.9082 | 0.7702 | 0.7648 | 0.8579 | 0.8456 | 0.8228 | — | fastText | 0.9203 | 0.9170 | 0.7706 | 0.7624 | 0.8670 | 0.8615 | 0.8181 | 0.8121 | LR-BiLSTM | — | — | — | — | — | — | 0.8210 | — | IDAN | 0.9297 | 0.9293 | 0.8005 | 0.7875 | 0.9181 | 0.9068 | 0.8266 | 0.8135 |
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Bold values indicate the best performances.
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