基于子事件的对话长文本情感分析
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杨京虎, 段亮, 岳昆, 李忠斌
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Sentimenta analysis based on subevents for long dialogue texts
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Jinghu Yang, Liang Duan, Kun Yue, Zhongbin Li
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表8 各模型对不同长度文本情感分析的准确率对比
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Table 8 Accuracy of different models with different length texts based sentiment analysis
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| 500~ 1000字 | 1000~2000字 | 2000~3000字 | 3000~4000字 | 4000~5000字 |
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| TER | 77.42% | 75.58% | 73.47% | 76.00% | 73.39% | | TextCNN | 50.00% | 41.33% | 40.00% | 43.33% | 45.00% | | TextRNN | 43.33% | 41.30% | 30.37% | 33.33% | 40.00% | | FastText | 50.00% | 40.62% | 43.75% | 41.25% | 43.75% | | DPCNN | 30.00% | 36.96% | 30.00% | 38.89% | 38.00% | | Text⁃RCNN | 56.67% | 49.13% | 43.33% | 42.22% | 49.17% | | Transformer | 53.33% | 38.16% | 43.33% | 43.33% | 45.89% | | TodKat | 56.11% | 54.17% | 52.47% | 54.00% | 55.16% | | BERT | 60.00% | 58.00% | 50.45% | 50.11% | 50.25% | | ERNIE | 60.33% | 58.70% | 50.65% | 50.49% | 50.67% |
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