Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (4): 1058-1064.DOI: 10.11772/j.issn.1001-9081.2023040497
Special Issue: 人工智能
• Artificial intelligence • Previous Articles Next Articles
Xianfeng YANG1(), Yilei TANG1, Ziqiang LI2
Received:
2023-04-28
Revised:
2023-06-13
Accepted:
2023-06-30
Online:
2024-04-22
Published:
2024-04-10
Contact:
Xianfeng YANG
About author:
YANG Xianfeng, born in 1974, M. S., professor. Her research interests include computer image processing, wisdom education.Supported by:
通讯作者:
杨先凤
作者简介:
杨先凤(1974—),女,四川南部人,教授,硕士,主要研究方向:计算机图像处理、智慧教育 565695835@qq.com基金资助:
CLC Number:
Xianfeng YANG, Yilei TANG, Ziqiang LI. Aspect-level sentiment analysis model based on alternating‑attention mechanism and graph convolutional network[J]. Journal of Computer Applications, 2024, 44(4): 1058-1064.
杨先凤, 汤依磊, 李自强. 基于交替注意力机制和图卷积网络的方面级情感分析模型[J]. 《计算机应用》唯一官方网站, 2024, 44(4): 1058-1064.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023040497
实验环境 | 具体信息 |
---|---|
操作系统 | Windows 10 |
CPU | Intel Xeon CPU E5-2678 v3 @2.50 GHz |
显卡 | NVIDA Tesla K80 |
内存 | 8 GB |
开发语言 | Python 3.7 |
开发平台 | PyTorch 1.7.1 |
开发工具 | PyCharm 2021.1.1 |
Tab. 1 Experimental environment
实验环境 | 具体信息 |
---|---|
操作系统 | Windows 10 |
CPU | Intel Xeon CPU E5-2678 v3 @2.50 GHz |
显卡 | NVIDA Tesla K80 |
内存 | 8 GB |
开发语言 | Python 3.7 |
开发平台 | PyTorch 1.7.1 |
开发工具 | PyCharm 2021.1.1 |
数据集 | #Pos | #Neu | #Neg | |
---|---|---|---|---|
Twitter[ | 训练集 | 1 561 | 3 127 | 1 560 |
测试集 | 173 | 346 | 173 | |
LAP14[ | 训练集 | 994 | 464 | 870 |
测试集 | 341 | 169 | 128 | |
REST14[ | 训练集 | 2 164 | 637 | 807 |
测试集 | 728 | 196 | 196 | |
REST15[ | 训练集 | 1 178 | 50 | 382 |
测试集 | 439 | 35 | 328 | |
REST16[ | 训练集 | 1 620 | 88 | 709 |
测试集 | 597 | 38 | 190 |
Tab. 2 Information of sample number for each dataset
数据集 | #Pos | #Neu | #Neg | |
---|---|---|---|---|
Twitter[ | 训练集 | 1 561 | 3 127 | 1 560 |
测试集 | 173 | 346 | 173 | |
LAP14[ | 训练集 | 994 | 464 | 870 |
测试集 | 341 | 169 | 128 | |
REST14[ | 训练集 | 2 164 | 637 | 807 |
测试集 | 728 | 196 | 196 | |
REST15[ | 训练集 | 1 178 | 50 | 382 |
测试集 | 439 | 35 | 328 | |
REST16[ | 训练集 | 1 620 | 88 | 709 |
测试集 | 597 | 38 | 190 |
参数 | 值 | 参数 | 值 |
---|---|---|---|
Learning Rate | 0.001 | Dropout | 0.3 |
Batch Size | 32 | initializer | xavier_uniform_ |
L2正则化 | 10-5 |
Tab. 3 Experimental parameter setting
参数 | 值 | 参数 | 值 |
---|---|---|---|
Learning Rate | 0.001 | Dropout | 0.3 |
Batch Size | 32 | initializer | xavier_uniform_ |
L2正则化 | 10-5 |
模型 | LAP14 | REST14 | REST15 | REST16 | ||||||
---|---|---|---|---|---|---|---|---|---|---|
准确率 | F1 | 准确率 | F1 | 准确率 | F1 | 准确率 | F1 | 准确率 | F1 | |
LSTM | 69.32 | 67.84 | 70.53 | 64.19 | 77.77 | 66.89 | 76.38 | 55.47 | 87.34 | 65.88 |
MemNet | 71.77 | 70.24 | 71.79 | 67.54 | 79.58 | 70.67 | 78.04 | 60.47 | 85.88 | 67.84 |
AOA | 71.92 | 69.56 | 72.93 | 67.82 | 80.36 | 70.62 | 78.60 | 58.89 | 87.82 | 70.76 |
IAN | 70.37 | 68.24 | 73.56 | 68.34 | 79.49 | 70.32 | 78.66 | 59.32 | 87.01 | 65.59 |
PAM* | — | — | 75.39 | 70.50 | 81.37 | 72.06 | 80.88 | 62.48 | 89.30 | 66.93 |
RMN* | — | — | 74.50 | 69.79 | 81.16 | 73.17 | 80.69 | 64.41 | 88.75 | 71.54 |
GL-GCN* | 73.26 | 71.26 | 76.91 | 72.76 | 82.11 | 73.46 | 80.81 | 64.99 | 88.47 | 69.64 |
AGCN* | 73.98 | 72.48 | 75.07 | 70.96 | 80.02 | 71.02 | 80.07 | 62.70 | 87.98 | 65.78 |
TD-GAT* | 72.20 | 70.45 | 75.24 | 70.74 | 81.32 | 71.12 | 80.38 | 60.50 | 87.71 | 67.87 |
SK-GCN* | 71.97 | 70.22 | 73.20 | 69.18 | 80.36 | 70.43 | 80.12 | 60.70 | 85.17 | 68.08 |
AA-GCN | 74.13 | 72.44 | 76.09 | 71.43 | 82.35 | 74.36 | 79.77 | 63.46 | 89.32 | 70.90 |
Tab. 4 Comparison experiment results of different models
模型 | LAP14 | REST14 | REST15 | REST16 | ||||||
---|---|---|---|---|---|---|---|---|---|---|
准确率 | F1 | 准确率 | F1 | 准确率 | F1 | 准确率 | F1 | 准确率 | F1 | |
LSTM | 69.32 | 67.84 | 70.53 | 64.19 | 77.77 | 66.89 | 76.38 | 55.47 | 87.34 | 65.88 |
MemNet | 71.77 | 70.24 | 71.79 | 67.54 | 79.58 | 70.67 | 78.04 | 60.47 | 85.88 | 67.84 |
AOA | 71.92 | 69.56 | 72.93 | 67.82 | 80.36 | 70.62 | 78.60 | 58.89 | 87.82 | 70.76 |
IAN | 70.37 | 68.24 | 73.56 | 68.34 | 79.49 | 70.32 | 78.66 | 59.32 | 87.01 | 65.59 |
PAM* | — | — | 75.39 | 70.50 | 81.37 | 72.06 | 80.88 | 62.48 | 89.30 | 66.93 |
RMN* | — | — | 74.50 | 69.79 | 81.16 | 73.17 | 80.69 | 64.41 | 88.75 | 71.54 |
GL-GCN* | 73.26 | 71.26 | 76.91 | 72.76 | 82.11 | 73.46 | 80.81 | 64.99 | 88.47 | 69.64 |
AGCN* | 73.98 | 72.48 | 75.07 | 70.96 | 80.02 | 71.02 | 80.07 | 62.70 | 87.98 | 65.78 |
TD-GAT* | 72.20 | 70.45 | 75.24 | 70.74 | 81.32 | 71.12 | 80.38 | 60.50 | 87.71 | 67.87 |
SK-GCN* | 71.97 | 70.22 | 73.20 | 69.18 | 80.36 | 70.43 | 80.12 | 60.70 | 85.17 | 68.08 |
AA-GCN | 74.13 | 72.44 | 76.09 | 71.43 | 82.35 | 74.36 | 79.77 | 63.46 | 89.32 | 70.90 |
模型 | LAP14 | REST14 | REST15 | REST16 | ||||||
---|---|---|---|---|---|---|---|---|---|---|
准确率 | F1 | 准确率 | F1 | 准确率 | F1 | 准确率 | F1 | 准确率 | F1 | |
AA-GCN | 74.13 | 72.44 | 75.55 | 71.43 | 82.35 | 74.36 | 79.77 | 63.46 | 89.12 | 70.90 |
mine w/o ac | 73.04 | 72.86 | 74.97 | 70.79 | 81.63 | 73.74 | 79.09 | 62.33 | 88.15 | 69.03 |
mine w/o aa | 71.19 | 69.04 | 75.18 | 71.17 | 81.31 | 72.86 | 78.04 | 62.71 | 87.77 | 67.74 |
mine w/o alter | 73.74 | 72.03 | 75.44 | 70.70 | 82.35 | 74.50 | 79.95 | 64.13 | 88.53 | 69.72 |
mine w/0 pw | 74.08 | 72.51 | 75.13 | 70.79 | 81.25 | 73.39 | 78.95 | 61.93 | 88.26 | 70.36 |
mine w/0 gcn | 72.54 | 70.97 | 72.94 | 68.94 | 79.46 | 69.18 | 79.27 | 62.08 | 86.85 | 65.14 |
mine w/0 mas | 72.83 | 71.54 | 73.77 | 69.61 | 80.30 | 70.49 | 79.95 | 62.43 | 88.31 | 66.24 |
Tab. 5 Ablation experiment results
模型 | LAP14 | REST14 | REST15 | REST16 | ||||||
---|---|---|---|---|---|---|---|---|---|---|
准确率 | F1 | 准确率 | F1 | 准确率 | F1 | 准确率 | F1 | 准确率 | F1 | |
AA-GCN | 74.13 | 72.44 | 75.55 | 71.43 | 82.35 | 74.36 | 79.77 | 63.46 | 89.12 | 70.90 |
mine w/o ac | 73.04 | 72.86 | 74.97 | 70.79 | 81.63 | 73.74 | 79.09 | 62.33 | 88.15 | 69.03 |
mine w/o aa | 71.19 | 69.04 | 75.18 | 71.17 | 81.31 | 72.86 | 78.04 | 62.71 | 87.77 | 67.74 |
mine w/o alter | 73.74 | 72.03 | 75.44 | 70.70 | 82.35 | 74.50 | 79.95 | 64.13 | 88.53 | 69.72 |
mine w/0 pw | 74.08 | 72.51 | 75.13 | 70.79 | 81.25 | 73.39 | 78.95 | 61.93 | 88.26 | 70.36 |
mine w/0 gcn | 72.54 | 70.97 | 72.94 | 68.94 | 79.46 | 69.18 | 79.27 | 62.08 | 86.85 | 65.14 |
mine w/0 mas | 72.83 | 71.54 | 73.77 | 69.61 | 80.30 | 70.49 | 79.95 | 62.43 | 88.31 | 66.24 |
例句 | 方面词 | 预测值 | 真实值 | |
---|---|---|---|---|
AOA | AA-GCN | |||
The staff should be a bit more friendly. | staff | positive | negative | negative |
Great food but the service was dreadful! | food | positive | positive | positive |
Did not enjoy the new Windows 8 and touchscreen functions. | Windows 8 | positive | negative | negative |
After numerous attempts of trying -LRB- including setting the clock in BIOS setup directly -RRB-, I gave up -LRB- I am a techie -RRB- | clock in BIOS setup | neutral | negative | negative |
Tab. 6 Sample analysis
例句 | 方面词 | 预测值 | 真实值 | |
---|---|---|---|---|
AOA | AA-GCN | |||
The staff should be a bit more friendly. | staff | positive | negative | negative |
Great food but the service was dreadful! | food | positive | positive | positive |
Did not enjoy the new Windows 8 and touchscreen functions. | Windows 8 | positive | negative | negative |
After numerous attempts of trying -LRB- including setting the clock in BIOS setup directly -RRB-, I gave up -LRB- I am a techie -RRB- | clock in BIOS setup | neutral | negative | negative |
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