Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (6): 1796-1802.DOI: 10.11772/j.issn.1001-9081.2022060891
Special Issue: 人工智能
• Artificial intelligence • Previous Articles Next Articles
Zhixiong ZHENG1,2, Jianhua LIU1,2(), Shuihua SUN1,2, Ge XU3, Honghui LIN1,2
Received:
2022-06-20
Revised:
2022-09-23
Accepted:
2022-10-11
Online:
2022-11-07
Published:
2023-06-10
Contact:
Jianhua LIU
About author:
ZHENG Zhixiong, born in 1996, M. S. candidate. His research interests include aspect-based sentiment analysis.Supported by:
郑智雄1,2, 刘建华1,2(), 孙水华1,2, 徐戈3, 林鸿辉1,2
通讯作者:
刘建华
作者简介:
郑智雄(1996—),男,福建莆田人,硕士研究生,CCF会员,主要研究方向:方面级情感分析基金资助:
CLC Number:
Zhixiong ZHENG, Jianhua LIU, Shuihua SUN, Ge XU, Honghui LIN. Aspect-based sentiment analysis model fused with multi-window local information[J]. Journal of Computer Applications, 2023, 43(6): 1796-1802.
郑智雄, 刘建华, 孙水华, 徐戈, 林鸿辉. 融合多窗口局部信息的方面级情感分析模型[J]. 《计算机应用》唯一官方网站, 2023, 43(6): 1796-1802.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022060891
数据集 | 积极 | 消极 | 中性 | |||
---|---|---|---|---|---|---|
训练 | 测试 | 训练 | 测试 | 训练 | 测试 | |
Restaurant | 2 164 | 727 | 807 | 196 | 637 | 196 |
Laptop | 976 | 337 | 851 | 128 | 455 | 167 |
1 507 | 172 | 1 528 | 169 | 3 016 | 336 |
Tab. 1 Experimental data statistical information
数据集 | 积极 | 消极 | 中性 | |||
---|---|---|---|---|---|---|
训练 | 测试 | 训练 | 测试 | 训练 | 测试 | |
Restaurant | 2 164 | 727 | 807 | 196 | 637 | 196 |
Laptop | 976 | 337 | 851 | 128 | 455 | 167 |
1 507 | 172 | 1 528 | 169 | 3 016 | 336 |
参数 | 值 | 说明 |
---|---|---|
BERT Model | bert-base-uncased | BERT模型版本 |
Embedding Dimension | 768 | 词嵌入维度 |
BERT Dropout | 0.1 | BERT随机失活比例 |
MWFLL Dropout | 0.1 | MWFLL随机失活比例 |
Number of GAT Head | 4 | GAT的注意力头数 |
Learning Rate | 2×10-5 | 学习率 |
L2 Regularization Term | 10-5 | L2正则化超参数 |
Tab. 2 Hyperparameter setting
参数 | 值 | 说明 |
---|---|---|
BERT Model | bert-base-uncased | BERT模型版本 |
Embedding Dimension | 768 | 词嵌入维度 |
BERT Dropout | 0.1 | BERT随机失活比例 |
MWFLL Dropout | 0.1 | MWFLL随机失活比例 |
Number of GAT Head | 4 | GAT的注意力头数 |
Learning Rate | 2×10-5 | 学习率 |
L2 Regularization Term | 10-5 | L2正则化超参数 |
参数 | 值 | 说明 |
---|---|---|
Operating System | Windows 10 | 操作系统 |
GPU | Nvidia RTX 3070 | 图像处理器 |
GPU Memory | 8.0GB | 图像处理器内存 |
Development Tool | PyCharm 2020.3.1 | 开发工具 |
Deep Learning Framework | PyTorch 1.10.0 | 深度学习框架 |
Tab. 3 Environment of experiments
参数 | 值 | 说明 |
---|---|---|
Operating System | Windows 10 | 操作系统 |
GPU | Nvidia RTX 3070 | 图像处理器 |
GPU Memory | 8.0GB | 图像处理器内存 |
Development Tool | PyCharm 2020.3.1 | 开发工具 |
Deep Learning Framework | PyTorch 1.10.0 | 深度学习框架 |
类别 | 模型 | Restaurant | Laptop | ||||
---|---|---|---|---|---|---|---|
Acc | MF1 | Acc | MF1 | Acc | MF1 | ||
w/o Syn | ATAE-LSTM | 77.20 | — | 68.70 | — | — | — |
IAN | 78.60 | — | 72.10 | — | — | — | |
MGAN | 81.25 | 71.94 | 75.39 | 72.47 | 72.54 | 70.81 | |
AEN | 80.98 | 72.14 | 73.51 | 69.04 | 72.83 | 69.81 | |
AEN-BERT | 83.12 | 73.76 | 79.93 | 76.31 | 74.71 | 73.13 | |
CapsNet | 80.79 | — | — | — | 79.78 | — | |
W Syn | PhraseRNN | 66.20 | 59.32 | — | — | — | — |
LSTM+SynAtt | 80.45 | 71.26 | 72.57 | 69.13 | — | — | |
TD-GAT | 81.20 | — | 74.00 | — | — | — | |
CDT | 82.30 | 74.02 | 77.19 | 72.99 | 74.66 | 73.66 | |
R-GAT+BERT | 85.44 | 79.17 | 78.01 | 75.57 | 75.93 | 74.60 | |
T-GCN | 86.12 | 79.95 | 80.32 | 76.82 | 76.45 | 75.25 | |
MWGAT | 87.21 | 81.93 | 81.56 | 78.64 | 76.74 | 75.49 |
Tab. 4 Experimental results of different models
类别 | 模型 | Restaurant | Laptop | ||||
---|---|---|---|---|---|---|---|
Acc | MF1 | Acc | MF1 | Acc | MF1 | ||
w/o Syn | ATAE-LSTM | 77.20 | — | 68.70 | — | — | — |
IAN | 78.60 | — | 72.10 | — | — | — | |
MGAN | 81.25 | 71.94 | 75.39 | 72.47 | 72.54 | 70.81 | |
AEN | 80.98 | 72.14 | 73.51 | 69.04 | 72.83 | 69.81 | |
AEN-BERT | 83.12 | 73.76 | 79.93 | 76.31 | 74.71 | 73.13 | |
CapsNet | 80.79 | — | — | — | 79.78 | — | |
W Syn | PhraseRNN | 66.20 | 59.32 | — | — | — | — |
LSTM+SynAtt | 80.45 | 71.26 | 72.57 | 69.13 | — | — | |
TD-GAT | 81.20 | — | 74.00 | — | — | — | |
CDT | 82.30 | 74.02 | 77.19 | 72.99 | 74.66 | 73.66 | |
R-GAT+BERT | 85.44 | 79.17 | 78.01 | 75.57 | 75.93 | 74.60 | |
T-GCN | 86.12 | 79.95 | 80.32 | 76.82 | 76.45 | 75.25 | |
MWGAT | 87.21 | 81.93 | 81.56 | 78.64 | 76.74 | 75.49 |
窗口尺寸 | 不同数据集上的预测准确率 | |
---|---|---|
Restaurant | Laptop | |
3 | 86.51 | 80.70 |
5 | 87.12 | 81.25 |
7 | 86.15 | 79.06 |
9 | 85.08 | 79.22 |
11 | 85.88 | 78.75 |
Tab. 5 Performance comparison results for different window sizes
窗口尺寸 | 不同数据集上的预测准确率 | |
---|---|---|
Restaurant | Laptop | |
3 | 86.51 | 80.70 |
5 | 87.12 | 81.25 |
7 | 86.15 | 79.06 |
9 | 85.08 | 79.22 |
11 | 85.88 | 78.75 |
窗口数 | 窗口尺寸组合 | 不同数据集上的预测准确率 | |
---|---|---|---|
Restaurant | Laptop | ||
1 | (5) | 87.12 | 81.25 |
2 | (3,5) | 87.21 | 81.56 |
3 | (3,5,7) | 86.86 | 80.78 |
4 | (3,5,7,9) | 86.60 | 79.69 |
5 | (3,5,7,9,11) | 84.71 | 77.81 |
Tab. 6 Experimental results for different numbers of windows
窗口数 | 窗口尺寸组合 | 不同数据集上的预测准确率 | |
---|---|---|---|
Restaurant | Laptop | ||
1 | (5) | 87.12 | 81.25 |
2 | (3,5) | 87.21 | 81.56 |
3 | (3,5,7) | 86.86 | 80.78 |
4 | (3,5,7,9) | 86.60 | 79.69 |
5 | (3,5,7,9,11) | 84.71 | 77.81 |
模型 | Restaurant | Laptop | ||
---|---|---|---|---|
Acc | MF1 | Acc | MF1 | |
w/o MWFLL | 86.32 | 79.58 | 80.16 | 76.93 |
w/o GAT | 85.61 | 78.78 | 78.91 | 74.92 |
MWGAT | 87.21 | 81.93 | 81.56 | 78.64 |
Tab. 7 Results of ablation study
模型 | Restaurant | Laptop | ||
---|---|---|---|---|
Acc | MF1 | Acc | MF1 | |
w/o MWFLL | 86.32 | 79.58 | 80.16 | 76.93 |
w/o GAT | 85.61 | 78.78 | 78.91 | 74.92 |
MWGAT | 87.21 | 81.93 | 81.56 | 78.64 |
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