Dependency type and distance enhanced aspect based sentiment analysis model

doi:10.11772/j.issn.1001-9081.2024081088

Journal of Computer Applications ›› 2025, Vol. 45 ›› Issue (8): 2507-2514.DOI: 10.11772/j.issn.1001-9081.2024081088

• Artificial intelligence • Previous Articles

Dependency type and distance enhanced aspect based sentiment analysis model

Biao ZHAO¹, Yuhua QIN¹(), Rongkun TIAN¹, Yuehang HU², Fangrui CHEN²

^1.School of Information Science and Technology，Qingdao University of Science and Technology，Qingdao Shandong 266061，China
^2.Technical Research Center，China Tobacco Yunnan Industrial Company Limited，Kunming Yunnan 650024，China

Received:2024-08-05 Revised:2024-10-21 Accepted:2024-11-04 Online:2024-11-19 Published:2025-08-10
Contact: Yuhua QIN
About author:ZHAO Biao， born in 2001， M. S. candidate. His research interests include intelligent information processing， emotion classification.
TIAN Rongkun， born in 1996， M. S. candidate. His research interests include intelligent information processing.
HU Yuehang， born in 1984， M. S.， engineer. His research interests include big data.
CHEN Fangrui， born in 1977， senior engineer. Her research interests include product digitalization， data mining.
Supported by:
Qingdao Science and Technology Benefiting the People Demonstration Project(23-2-8-smjk-20-nsh)

依赖类型及距离增强的方面级情感分析模型

赵彪¹, 秦玉华¹(), 田荣坤¹, 胡月航², 陈芳锐²

^1.青岛科技大学信息科学技术学院，山东青岛 266061
^2.云南中烟工业责任有限公司技术中心，昆明 650024

通讯作者: 秦玉华
作者简介:赵彪（2001—），男，山东菏泽人，硕士研究生，主要研究方向：智能信息处理、情感分类
田荣坤（1996—），男，山东滨州人，硕士研究生，CCF会员，主要研究方向：智能信息处理
胡月航（1984—），男，云南昆明人，工程师，硕士，主要研究方向：大数据
陈芳锐（1977—），女，云南曲靖人，高级工程师，主要研究方向：产品数字化、数据挖掘。
基金资助:
青岛市科技惠民示范项目(23-2-8-smjk-20-nsh)

Abstract

Abstract:

Aspect-Based Sentiment Analysis （ABSA） tasks aim to determine the sentiment polarity of specific aspect words in comments. In the field of ABSA， dual-channel models that extract both grammar and semantic information have achieved certain results. However， the existing models fail to consider the different degrees of importance among grammar nodes， the additional noise introduced by global attention mechanism， and the existence of correlations between similar features comprehensively. To address these issues， a dual-channel graph convolutional model with dependency type and distance enhancements was proposed. Firstly， dependency types were introduced in the grammar module to measure the importance of neighborhood nodes. Secondly， mask matrices based on the dependency tree distance were constructed to filter out grammar unrelated noise. Finally， a supervised contrastive loss was introduced to facilitate the model to learn correlations between similar features. Experimental results show that on SemEval-2014 Restaurant， SemEval-2014 Laptop and Twitter datasets， compared to the second-best model DGNN （Dual Graph Neural Network）， the proposed model achieves accuracy improvements of 0.11， 0.94， 1.01 percentage points， respectively， and Macro-F1 improvements of 0.63， 1.66， 0.83 percentage points， respectively， verifying the effectiveness of the proposed model.

Key words: Aspect-Based Sentiment Analysis (ABSA), graph convolutional network, dependency type, dependency tree distance, supervised contrastive loss

摘要：

方面级情感分析（ABSA）任务旨在判断评论语句中特定方面词的情感极性。在ABSA领域中，同时提取语法和语义这2种信息的双通道模型取得了一定的效果。然而，现有模型未能考虑语法节点间的重要程度不同、全局范围下的注意力机制引入的额外噪声以及同类特征间存在一定关联性等问题。为了解决以上问题，提出一种依赖类型及距离增强的双通道图卷积模型。首先，在语法模块引入依赖类型以衡量不同邻近节点的重要程度；其次，以依赖树距离为依据构造掩码矩阵进而过滤与语法无关的噪声；最后，引入一个有监督对比损失帮助模型学习同类特征间的关联性。实验结果表明，相较于次优模型DGNN（Dual Graph Neural Network），所提模型在SemEval-2014 Restaurant、SemEval-2014 Laptop和Twitter这3个数据集上分别取得了0.11、0.94和1.01个百分点的准确率提升，以及0.63、1.66和0.83个百分点的宏F1值提升，验证了所提模型的有效性。

关键词: 方面级别情感分析, 图神经网络, 依赖类型, 依赖树距离, 有监督对比损失

CLC Number:

TP391.1

Biao ZHAO, Yuhua QIN, Rongkun TIAN, Yuehang HU, Fangrui CHEN. Dependency type and distance enhanced aspect based sentiment analysis model[J]. Journal of Computer Applications, 2025, 45(8): 2507-2514.

赵彪, 秦玉华, 田荣坤, 胡月航, 陈芳锐. 依赖类型及距离增强的方面级情感分析模型[J]. 《计算机应用》唯一官方网站, 2025, 45(8): 2507-2514.

Figures/Tables 10

References 34

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情感类别	Restaurant		Laptop		Twitter
情感类别	训练集	测试集	训练集	测试集	训练集	测试集
积极	2 164	727	976	337	1 507	172
中性	637	196	455	167	3 016	336
消极	807	196	851	128	1 528	169

情感类别	Restaurant		Laptop		Twitter
情感类别	训练集	测试集	训练集	测试集	训练集	测试集
积极	2 164	727	976	337	1 507	172
中性	637	196	455	167	3 016	336
消极	807	196	851	128	1 528	169

类别	模型	Restaurant		Laptop		Twitter
类别	模型	ACC	宏F1	ACC	宏F1	ACC	宏F1
上下文	TD-LSTM	75.60	64.51	70.81	69.11	70.80	69.00
上下文	GCAE	79.14	68.53	71.73	66.04	72.45	70.87
语义	IAN	79.26	70.09	72.05	67.38	72.50	70.81
语义	AOA	79.97	70.42	72.62	67.52	72.30	70.20
语法	ASGCN	80.77	72.02	75.55	71.05	72.15	70.40
	Sentic GCN	84.03	75.38	77.90	74.71	—	—
	CRF-GCN	82.71	73.87	75.83	74.78	—	—
多通道	DGEDT	83.90	75.10	76.80	72.30	74.80	73.40
	R-GAT	83.30	76.08	77.42	73.76	75.57	73.82
	DGNN	84.25	77.05	77.86	74.09	75.36	74.33
	TCKGCN	—	—	78.50	74.21	75.92	74.26
	本文模型	84.36	77.68	78.80	75.75	76.37	75.16

类别	模型	Restaurant		Laptop		Twitter
类别	模型	ACC	宏F1	ACC	宏F1	ACC	宏F1
上下文	TD-LSTM	75.60	64.51	70.81	69.11	70.80	69.00
上下文	GCAE	79.14	68.53	71.73	66.04	72.45	70.87
语义	IAN	79.26	70.09	72.05	67.38	72.50	70.81
语义	AOA	79.97	70.42	72.62	67.52	72.30	70.20
语法	ASGCN	80.77	72.02	75.55	71.05	72.15	70.40
	Sentic GCN	84.03	75.38	77.90	74.71	—	—
	CRF-GCN	82.71	73.87	75.83	74.78	—	—
多通道	DGEDT	83.90	75.10	76.80	72.30	74.80	73.40
	R-GAT	83.30	76.08	77.42	73.76	75.57	73.82
	DGNN	84.25	77.05	77.86	74.09	75.36	74.33
	TCKGCN	—	—	78.50	74.21	75.92	74.26
	本文模型	84.36	77.68	78.80	75.75	76.37	75.16

模型	Restaurant		Laptop		Twitter
模型	ACC	宏F1	ACC	宏F1	ACC	宏F1
原模型	84.36	77.68	78.80	75.75	76.37	75.16
w/o DepType	83.20	75.76	77.53	74.25	75.48	73.84
w/o synMask	83.91	75.94	77.06	73.51	75.63	73.88
w/o SCLoss	83.65	76.66	78.01	74.55	75.18	74.02

Dependency type and distance enhanced aspect based sentiment analysis model

依赖类型及距离增强的方面级情感分析模型

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