融合句法增强与语义增强的方面情感分析

刘耀; 吴云飞; 周红静; 黄少年; 张震

doi:10.11959/j.issn.2096-6652.202505

您当前的位置：

首页 >

文章列表页 >

融合句法增强与语义增强的方面情感分析

学术论文 | 更新时间：2025-06-10

- 融合句法增强与语义增强的方面情感分析
- Fusion of syntactic enhancement and semantic enhancement for aspect-based sentiment analysis
- 智能科学与技术学报 2025年7卷第1期页码：54-63
- 作者机构：
  
  1.湖南工商大学长沙人工智能社会实验室，湖南长沙 410205
  2.湖南工商大学数字媒体工程与人文学院，湖南长沙 410205
  3.湖南工商大学计算机学院，湖南长沙 410205
- 作者简介：
  
  [ "刘耀（1976- ），男，博士，湖南工商大学数字媒体工程与人文学院副教授，主要研究方向为情感计算、边缘智能。" ]
  [ "吴云飞（1999- ），男，湖南工商大学计算机学院硕士生，主要研究方向为情感分析、机器学习。" ]
  [ "周红静（1976- ），女，湖南工商大学计算机学院讲师，主要研究方向为机器学习、边缘智能。" ]
  [ "黄少年（1977- ），女，博士，湖南工商大学计算机学院副教授，主要研究方向为机器学习、行为分析与建模。" ]
  [ "张震（1994- ），男，博士，湖南工商大学长沙人工智能社会实验室副教授，主要研究方向为理论计算、机器学。" ]
- 基金信息：
  
  长沙人工智能社会实验室资助项目;国家自然科学基金项目(62202161);湖南省教育厅科学研究项目(21A0376);湖南省社会科学成果评审委员会课题(XSP25YBC164)
- DOI：10.11959/j.issn.2096-6652.202505
  中图分类号： TP391.1
- 收稿日期：2024-09-27，
  
  修回日期：2025-01-03，
  
  纸质出版日期：2025-03-15
- 稿件说明：
移动端阅览
刘耀,吴云飞,周红静等.融合句法增强与语义增强的方面情感分析[J].智能科学与技术学报,2025,07(01):54-63.

LIU Yao,WU Yunfei,ZHOU Hongjing,et al.Fusion of syntactic enhancement and semantic enhancement for aspect-based sentiment analysis[J].Chinese Journal of Intelligent Science and Technology,2025,07(01):54-63.
刘耀,吴云飞,周红静等.融合句法增强与语义增强的方面情感分析[J].智能科学与技术学报,2025,07(01):54-63. DOI： 10.11959/j.issn.2096-6652.202505.

LIU Yao,WU Yunfei,ZHOU Hongjing,et al.Fusion of syntactic enhancement and semantic enhancement for aspect-based sentiment analysis[J].Chinese Journal of Intelligent Science and Technology,2025,07(01):54-63. DOI： 10.11959/j.issn.2096-6652.202505.

摘要

使用图神经网络在句子的句法依赖树上建模进行方面情感分析时，主要关注句子的句法结构，但是忽视了对依赖树本身以及语义信息的研究，同时非结构化文本中句法信息会带来额外噪声。针对上述问题，提出一种融合句法增强与语义增强的图卷积网络模型，使用注意力机制提取方面信息和全局信息并通过门控机制结合增强语义信息，同时利用外部情感知识以及单词的距离特征重塑依赖树来提高意见词权重增强句法信息，最后对句法信息和语义信息进行动态特征融合。在3组公开数据集上进行了大量的实验，结果表明，提出的模型准确率和宏F1值相较于对比模型更高，验证了融合句法增强和语义增强在方面情感分析中的有效性。

Abstract

The graph neural networks mainly focus on the syntactic structure when they are used to model the syntactic dependency tree of a sentence for aspect-based sentiment analysis (ABSA). However

these methods neglect the study of dependency tree itself and semantic information

and syntactic information in unstructured text can bring additional noise. To address these issues

a graph convolutional network (GCN) model integrating syntactic and semantic enhancements was proposed. Attention mechanism was used to extract aspect information and global information

and enhance semantic information through gating mechanism. Simultaneously

dependency tree was reshaped to increase the weight of opinion words and enhance syntactic information by utilizing external emotional knowledge and the distance features of words. Finally

dynamic feature fusion was performed on semantic and syntactic information. Extensive experiments were conducted on three public datasets. The experiment results show that the accuracy and macro-F1 values are better than the compared models

which indicate the effectiveness of the fusion of syntactic enhancement and semantic enhancement for ABSA.

关键词

Keywords

references

张文轩, 殷雁君. 用于方面级情感分析的图指导的差异化注意力网络[J]. 中文信息学报, 2023, 37(7): 102-113.

ZHANG W X, YIN Y J. Graph-guided differentiated attention network for aspect-level sentiment analysis[J]. Journal of Chinese Information Processing, 2023, 37(7): 102-113.

陈壮, 钱铁云, 李万理, 等. 低资源方面级情感分析研究综述[J]. 计算机学报, 2023, 46(7): 1445-1472.

CHEN Z, QIAN T Y, LI W L, et al. Low-resource aspect-based sentiment analysis: a survey[J]. Chinese Journal of Computers, 2023, 46(7): 1445-1472.

MALOUF R, MULLEN T. Taking sides: user classification for informal online political discourse[J]. Internet Research, 2008, 18(2): 177-190.

WANG H N, LU Y, ZHAI C X, et al. Latent aspect rating analysis without aspect keyword supervision[C]//Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2011: 618-626.

TANG D, QIN B, FENG X C, et al. Effective LSTMs for target-dependent sentiment classification[C]//Proceedings of the 26th International Conference on Computational Linguistics. New York: ACM, 2016: 3298-3307.

RUDER S, GHAFFARI P, BRESLIN J G. A hierarchical model of reviews for aspect-based sentiment analysis[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: ACL, 2016: 999-1005.

HUANG B X, CARLEY K. Parameterized convolutional neural networks for aspect level sentiment classification[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: ACL, 2018: 1091-1096.

WANG Y Q, HUANG M L, ZHU X Y, et al. Attention-based LSTM for aspect-level sentiment classification[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: ACL, 2016: 606-615.

MA D H, LI S J, ZHANG X D, et al. Interactive attention networks for aspect-level sentiment classification[C]//Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. Menlo Park: AAAI press, 2017: 4068-4074.

徐志栋, 陈炳阳, 王晓, 等. 基于胶囊网络的方面级情感分类研究[J]. 智能科学与技术学报, 2020, 2(3): 284-292.

XU Z D, CHEN B Y, WANG X, et al. Research on capsule network-based for aspect-level sentiment classification[J]. Chinese Journal of Intelligent Science and Technology, 2020, 2(3): 284-292.

ZHANG C, LI Q C, SONG D W. Aspect-based sentiment classification with aspect-specific graph convolutional networks[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Stroudsburg: ACL, 2019: 4568-4578.

WANG K, SHEN W Z, YANG Y Y, et al. Relational graph attention network for aspect-based sentiment analysis[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: ACL, 2020: 3229-3238.

ZHAO P L, HOU L L, WU O. Modeling sentiment dependencies with graph convolutional networks for aspect-level sentiment classification[J]. Knowledge-Based Systems, 2020, 193: 105443.

ZHOU J, HUANG J X, HU Q V, et al. SK-GCN: modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification[J]. Knowledge-Based Systems, 2020, 205: 106292.

LI R F, CHEN H, FENG F X, et al. DualGCN: exploring syntactic and semantic information for aspect-based sentiment analysis[J]. IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(6): 7642-7656.

LIU H T, WU Y M, LI Q Y, et al. Enhancing aspect-based sentiment analysis using a dual-gated graph convolutional network via contextual affective knowledge[J]. Neurocomputing, 2023, 553: 126526.

WANG Z H, ZHANG B, YANG R, et al. DAGCN: distance-based and aspect-oriented graph convolutional network for aspect-based sentiment analysis[C]//Proceedings of the Findings of the Association for Computational Linguistics. Stroudsburg: ACL, 2024: 1863-1876.

PENNINGTON J, SOCHER R, MANNING C. GloVe: global vectors for word representation[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Stroudsburg: ACL, 2014: 1532-1543.

DEVLIN J, CHANG M W, LEE K, et al. BERT: pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, Minnesota. Stroudsburg: ACL, 2019: 4171-4186.

ZHANG Z, ZHOU Z L, WANG Y N. SSEGCN: syntactic and semantic enhanced graph convolutional network for aspect-based sentiment analysis[C]//Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg: ACL, 2022: 4916-4925.

LIANG B, SU H, GUI L, et al. Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks[J]. Knowledge-Based Systems, 2022, 235: 107643.

夏鸿斌, 李强, 刘渊. 局部与全局特征融合的方面情感分析网络模型[J]. 计算机科学与探索, 2023, 17(4): 902-911.

XIA H B, LI Q, LIU Y. Local and global feature fusion network model for aspect-based sentiment analysis[J]. Journal of Frontiers of Computer Science and Technology, 2023, 17(4): 902-911.

PONTIKI M, GALANIS D, PAVLOPOULOS J, et al. SemEval-2014 task 4: aspect based sentiment analysis[C]//Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014). Stroudsburg: ACL, 2014: 27-35.

DONG L, WEI F R, TAN C Q, et al. Adaptive recursive neural network for target-dependent Twitter sentiment classification[C]//Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Stroudsburg: ACL, 2014: 49-54.

HUANG B X, OU Y L, CARLEY K M. Aspect level sentiment classification with attention-over-attention neural networks[C]//Proceedings of Social, Cultural, and Behavioral Modeling: 11th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation. Cham: Springer International Publishing, 2018: 197-206.

FAN F F, FENG Y S, ZHAO D Y. Multi-grained attention network for aspect-level sentiment classification[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: ACL, 2018: 3433-3442.

MA F K, HU X M, LIU A W, et al. AMR-based network for aspect-based sentiment analysis[C]//Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg: ACL, 2023: 322-337.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于改进YOLOv5s的小人脸检测

基于稠密块和注意力机制的肺部病理图像异常细胞分割

基于情感信息融合注意力机制的抑郁症识别

基于角度敏感的空间注意力机制的轻量型旋转目标检测器

基于差分特征注意力机制的无锚框多光谱行人检测算法