Aspect-level sentiment analysis model based on alternating‑attention mechanism and graph convolutional network

doi:10.11772/j.issn.1001-9081.2023040497

Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (4): 1058-1064.DOI: 10.11772/j.issn.1001-9081.2023040497

• Artificial intelligence • Previous Articles

Aspect-level sentiment analysis model based on alternating‑attention mechanism and graph convolutional network

Xianfeng YANG¹(), Yilei TANG¹, Ziqiang LI²

^1.School of Computer Science and Software Engineering，Southwest Petroleum University，Chengdu Sichuan 610500，China
^2.College of Movie and Media，Sichuan Normal University，Chengdu Sichuan 610066，China

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.
TANG Yilei， born in 2000， M. S. candidate. His research interests include natural language processing.
LI Ziqiang， born in 1970，Ph. D.， professor. His research interests include machine learning， wisdom education， natural language processing.
Supported by:
National Natural Science Foundation of China(61802321);Key Research and Development Program of Science and Technology Department of Sichuan Province(2020YFN0019)

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

杨先凤¹(), 汤依磊¹, 李自强²

^1.西南石油大学计算机与软件学院，成都 610500
^2.四川师范大学影视与传媒学院，成都 610066

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
汤依磊（2000—），男，重庆人，硕士研究生，主要研究方向：自然语言处理
李自强（1970—），男，四川青神人，教授，博士，CCF会员，主要研究方向：机器学习、智慧教育、自然语言处理。
基金资助:
国家自然科学基金资助项目(61802321);四川省科技厅重点研发计划项目(2020YFN0019)

Abstract

Abstract:

Aspect-level sentiment analysis aims to predict the sentiment polarity of specific target in given text. Aiming at the problem of ignoring the syntactic relationship between aspect words and context and reducing the attention difference caused by average pooling， an aspect-level sentiment analysis model based on Alternating-Attention （AA） mechanism and Graph Convolutional Network （AA-GCN） was proposed. Firstly， the Bidirectional Long Short-Term Memory （Bi-LSTM） network was used to semantically model context and aspect words. Secondly， the GCN based on syntactic dependency tree was used to learn location information and dependencies， and the AA mechanism was used for multi-level interactive learning to adaptively adjust the attention to the target word. Finally， the final classification basis was obtained by splicing the corrected aspect features and context features. Compared with the Target-Dependent Graph Attention Network （TD-GAT）， the accuracies of the proposed model on four public datasets increased by 1.13%-2.67%， and the F1 values on five public datasets increased by 0.98%-4.89%， indicating the effectiveness of using syntactic relationships and increasing keyword attention.

Key words: Natural Language Processing (NLP), deep learning, aspect-level sentiment analysis, Alternating?Attention (AA) mechanism, Graph Convolutional Network (GCN)

摘要：

方面级情感分析旨在预测给定文本中特定目标的情感极性。针对忽略方面词和上下文之间的句法关系和平均池化带来的注意力差异性变小的问题，提出一种基于交替注意力（AA）机制和图卷积网络（GCN）的方面级情感分析模型（AA-GCN）。首先，利用双向长短期记忆（Bi-LSTM）网络对上下文和方面词进行语义建模；其次，通过基于句法依存树的GCN学习位置信息和依赖关系，再利用AA机制进行多层次交互学习，自适应地调整对目标词的关注度；最后，拼接修正后的方面特征和上下文特征，得到最终的分类依据。相较于基于目标依赖的图注意力网络（TD-GAT），所提模型在4个公开数据集上准确率提升了1.13%~2.67%，在5个公开数据集上F1值提升了0.98%~4.89%，验证了利用句法关系和提升关键词关注度的有效性。

关键词: 自然语言处理, 深度学习, 方面级情感分析, 交替注意力机制, 图卷积网络

CLC Number:

TP391.1

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.

Figures/Tables 8

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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

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^［4］	训练集	1 561	3 127	1 560
Twitter^［4］	测试集	173	346	173
LAP14^［29］	训练集	994	464	870
LAP14^［29］	测试集	341	169	128
REST14^［29］	训练集	2 164	637	807
REST14^［29］	测试集	728	196	196
REST15^［30］	训练集	1 178	50	382
REST15^［30］	测试集	439	35	328
REST16^［31］	训练集	1 620	88	709
REST16^［31］	测试集	597	38	190

数据集		#Pos	#Neu	#Neg
Twitter^［4］	训练集	1 561	3 127	1 560
Twitter^［4］	测试集	173	346	173
LAP14^［29］	训练集	994	464	870
LAP14^［29］	测试集	341	169	128
REST14^［29］	训练集	2 164	637	807
REST14^［29］	测试集	728	196	196
REST15^［30］	训练集	1 178	50	382
REST15^［30］	测试集	439	35	328
REST16^［31］	训练集	1 620	88	709
REST16^［31］	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

Aspect-level sentiment analysis model based on alternating‑attention mechanism and graph convolutional network

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

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Figures/Tables 8

References 37

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Metrics

模型	Twitter		LAP14		REST14		REST15		REST16
模型	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

例句	方面词	预测值		真实值
例句	方面词	AOA	AA-GCN	真实值
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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模型	Twitter		LAP14		REST14		REST15		REST16
模型	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

模型	Twitter		LAP14		REST14		REST15		REST16
模型	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