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面向方面情感分析的多通道增强图卷积网络

韩虎 范雅婷 徐学锋

韩虎, 范雅婷, 徐学锋. 面向方面情感分析的多通道增强图卷积网络[J]. 电子与信息学报, 2024, 46(3): 1022-1032. doi: 10.11999/JEIT230353
引用本文: 韩虎, 范雅婷, 徐学锋. 面向方面情感分析的多通道增强图卷积网络[J]. 电子与信息学报, 2024, 46(3): 1022-1032. doi: 10.11999/JEIT230353
HAN Hu, FAN Yating, XU Xuefeng. Multi-channel Enhanced Graph Convolutional Network for Aspect-based Sentiment Analysis[J]. Journal of Electronics & Information Technology, 2024, 46(3): 1022-1032. doi: 10.11999/JEIT230353
Citation: HAN Hu, FAN Yating, XU Xuefeng. Multi-channel Enhanced Graph Convolutional Network for Aspect-based Sentiment Analysis[J]. Journal of Electronics & Information Technology, 2024, 46(3): 1022-1032. doi: 10.11999/JEIT230353

面向方面情感分析的多通道增强图卷积网络

doi: 10.11999/JEIT230353
基金项目: 国家自然科学基金(62166024)
详细信息
    作者简介:

    韩虎:男,教授,研究方向为神经网络与深度学习、数据挖掘与自然语言处理

    范雅婷:女,硕士生,研究方向为深度学习与自然语言处理

    徐学锋:男,硕士生,研究方向为深度学习与自然语言处理

    通讯作者:

    范雅婷 1660296291@qq.com

  • 中图分类号: TN911.7; TP391.1

Multi-channel Enhanced Graph Convolutional Network for Aspect-based Sentiment Analysis

Funds: The National Natural Science Foundation of China (62166024)
  • 摘要: 传统的基于单通道的特征提取方式,仅使用单一的依赖关系捕获特征,忽略单词间的语义相似性与依赖关系类型信息。尽管基于图卷积网络进行方面情感分析的方法已经取得一定成效,但始终难以同时聚合节点的语义信息和句法结构特征,在整个迭代训练过程中最初的语义特征会逐渐遗失,影响句子最终的情感分类效果。由于缺乏先验知识会导致模型对相关情感词的误解,因此需要引入外部知识来丰富文本信息。目前,如何利用图神经网络(GNN)融合句法和语义特征的方式仍值得深入研究。针对上述问题,该文提出一种多通道增强图卷积网络模型。首先,通过对情感知识和依赖类型增强的句法图进行图卷积操作,得到基于语法的两种表示,与经过多头注意力和图卷积学习到的语义表示进行融合,使多通道的特征能够互补学习。实验结果表明,在5个公开数据集上,准确率和宏F1值优于基准模型。由此可见,依赖类型和情感知识均对增强句法图有重要影响,表明融合语义信息与句法结构的有效性。
  • 图  1  KSD-GCN模型结构示意图

    图  2  语义图卷积示意图

    图  3  GCN网络层数与准确率和宏F1值的关系

    图  4  自注意力头数与准确率和宏F1值的关系

    图  5  Top-k选择个数与准确率和宏F1值的关系

    图  6  方面词“mountain lion”的多通道注意力可视化

    图  7  方面词“milkshakes”的多通道注意力可视化

    表  1  SenticNet 6中情感词汇示例

    词汇极性-标签SenticNet情感得分语义相关词汇
    Great积极0.999'adore', 'glory', 'devote', 'admire', 'fairly'
    Excellent积极0.795'well_done', 'great', 'excelent', 'good_quality', 'good'
    Hard 消极 –0.291'hard_nail', 'disturb', 'upset', 'perturb', 'frustrate'
    Belittlement消极–0.693'revile', 'rude', 'vituperation', 'scurrility', 'billingsgate'
    Unsalable 消极–0.934'sorry', 'trash', 'rubbish', 'superfluous', 'otiose'
    下载: 导出CSV

    表  2  数据集统计

    数据集积极中性消极
    训练集测试集训练集测试集训练集测试集
    Twitter156117331273461560173
    Lap14994341464169870128
    Rest142164728637196807196
    Rest159123263634256182
    Rest1612404696930439117
    下载: 导出CSV

    表  3  模型的超参数设置

    超参数GloVe参数设置BERT参数设置
    批量训练样本数Batch size1616
    训练迭代次数Epoch100100
    优化器OptimizerAdamAdam
    学习率Learning rate10–32×10–5
    丢失率Dropout rate0.30.2
    L2正则化系数10–510–3
    下载: 导出CSV

    表  4  不同模型的结果对比(%)

    类别模型TwitterLap 14Rest 14Rest 15Rest16
    Acc宏F1Acc宏F1Acc宏F1Acc宏F1Acc宏F1
    基线LSTM69.5667.7069.2863.0978.1367.4777.3755.1786.8063.88
    ATAE-LSTM69.6567.4069.1463.1877.3266.5775.4356.3483.2563.85
    IAN72.5070.8172.0567.3879.2670.0978.5452.6584.7455.21
    AF-LSTM69.2168.2469.9763.4977.4665.1876.1256.2985.6166.15
    GCN模型TD-GAT72.7070.4575.6370.6481.3271.7280.3860.5087.7167.87
    ASGCN72.1570.4075.5571.0580.7772.0279.8961.8988.9967.48
    BiGCN74.1673.3574.4971.8481.9773.4881.1664.7988.9670.84
    GL-GCN73.2671.2676.9172.7682.1173.4680.8164.9988.4769.64
    MIGCN73.3172.1276.5972.4482.3274.3180.8164.2189.5071.97
    DA-GCN72.7870.9375.5871.2081.4274.6279.7061.8889.7667.81
    MSD-GCN74.5273.6877.5974.1882.6775.4480.4467.7489.0371.95
    SGAN74.9573.3776.7572.9081.8074.2881.2865.3188.4972.83
    KSD-GCN75.0073.3878.3775.1884.1176.7882.4764.2090.4273.81
    BERTDA-GCN-BERT75.4373.6778.8275.2883.4374.3582.9764.5689.6971.86
    SK-GCN-BERT75.0073.0179.0075.5783.4875.1983.2066.7887.1972.02
    KSD-GCN-BERT76.7375.7680.7276.5886.0780.0084.5068.8391.8874.09
    下载: 导出CSV

    表  5  消融实验结果(%)

    模型TwitterLap 14Rest 14Rest 15Rest16
    Acc宏F1Acc宏F1Acc宏F1Acc宏F1Acc宏F1
    本文75.0073.3878.3775.1884.1176.7882.4764.2090.4273.81
    w/o sem72.2570.5875.7071.2881.3371.5981.3662.1288.3168.10
    w/o dsyn73.5571.5074.6070.1081.5171.4780.8161.6187.6667.73
    w/o ksyn73.1271.9274.6170.2381.6072.0880.2563.7788.9668.51
    w/o sem-dsyn72.5470.7875.3970.7381.1671.1280.9962.4488.3170.11
    w/o dsyn-ksyn72.9771.4475.5470.4881.0770.2180.4462.0387.6668.54
    w/o sem-ksyn72.5470.7974.6071.2280.9871.1979.7062.0188.1469.46
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-05-04
  • 修回日期:  2023-09-24
  • 网络出版日期:  2023-09-28
  • 刊出日期:  2024-03-27

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