基于情感增强与双图卷积网络的方面级情感分析

doi:10.19678/j.issn.1000-3428.0067847

摘要/Abstract

摘要： 方面级情感分析旨在检测给定方面句子的情感极性。现有研究大多在句法依存树上构造图卷积网络,以获取方面词与上下文之间的句法信息。然而这类方法存在提取信息不够丰富、缺乏对句子中情感信息的挖掘等问题。针对上述问题,提出基于情感增强与双图卷积网络的方面级情感分析模型。该模型由双通道图卷积网络组成,旨在挖掘句子中的情感信息、句法信息和语义信息。利用位置信息和情感知识在依存树上构造情感增强依存图,并以此构建情感增强图卷积网络,增强方面词与上下文之间的情感依赖关系,同时挖掘句子中丰富的句法信息特征。构建基于多头注意力机制的图卷积网络,获取句子中的语义特征信息。对双图卷积网络的输出特征进行掩码、平均池化和拼接等操作,并通过情感分类层进行分类。实验结果表明,该模型与经典的图卷积网络模型(ASGCN)相比,在Restaurant、Laptop和Twitter数据集上的准确率和F1值分别提升3.43和5.69、3.13和3.92、3.57和4.02个百分点,具有较好的情感分类性能。

关键词: 方面级情感分析, 情感增强, 依存关系, 图卷积网络, 多头注意力机制

Abstract: Aspect Level Sentiment Analysis (ALSA) is designed to detect the sentiment polarity aspect of a given sentence. Most existing studies have reported the construction of Graph Convolutional Networks (GCN) on syntactic dependency trees to obtain syntactic information from aspect words and their contexts. However, this method has problems such as insufficient information extraction and a lack of sentiment information mining from sentences. To solve these problems, an ALSA model based on a sentiment-enhanced and dual-graph convolutional network is proposed. The model consists of a two-channel GCN that aims to mine the sentiment, syntactic, and semantic information in sentences. Position information and sentiment knowledge are used to construct a sentiment-enhanced dependency graph on syntactic dependency trees, and then a convolutional network of sentiment-enhanced graphs is constructed to enhance the sentiment-dependent relationship between aspect words and context, while simultaneously mining the rich syntactic information features in sentences. Subsequently, a GCN based on a multi-head attention mechanism is constructed to obtain the semantic information features in sentences. The output features of the dual GCN are masked, average-pooled, concatenated, and classified by the sentiment classification layer. The experimental results show that compared with the classical GCN model (ASGCN), the accuracy and F1 value of the Restaurant, Laptop and Twitter datasets improved by 3.43, 5.69, 3.13, 3.92, 3.57, and 4.02 percentage points, respectively. The proposed model has a better sentiment classification performance.

Key words: Aspect Level Sentiment Analysis(ALSA), sentiment-enhanced, dependency relationship, Graph Convolutional Network(GCN), multi-head attention mechanism

中图分类号:

TP391

代巍, 王丰羽, 冀常鹏. 基于情感增强与双图卷积网络的方面级情感分析[J]. 计算机工程, 2024, 50(5): 120-127.

DAI Wei, WANG Fengyu, JI Changpeng. Aspect Level Sentiment Analysis Based on Sentiment-Enhanced and Dual Graph Convolutional Network[J]. Computer Engineering, 2024, 50(5): 120-127.

https://www.ecice06.com/CN/Y2024/V50/I5/120

参考文献

[1] LI H H, XUE Y, ZHAO H Y, et al. Co-attention networks for aspect-level sentiment analysis[C]//Proceedings of International Conference on Natural Language Processing and Chinese Computing. Berlin, Germany:Springer, 2019:200-209.
[2] TRANG P H, THANH N N, DOSAM H. Aspect-level sentiment analysis:a survey of graph convolutional network methods[J]. Information Fusion, 2023, 91:149-172.
[3] JIANG L, YU M, ZHOU M, et al. Target-dependent Twitter sentiment classification[C]//Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics:Human Language Technologies. New York, USA:ACM Press, 2011:151-160.
[4] TANG D Y, QIN B, FENG X C, et al. Effective LSTMs for target-dependent sentiment classification[EB/OL].[2023-05-08]. https://arxiv.org/pdf/1512.01100.pdf.
[5] MA D, LI S, ZHANG X, et al. Interactive attention networks for aspect-level sentiment classification[C]//Proceedings of International Joint Conference on Artificial Intelligence. Washington D.C., USA:IEEE Press, 2017:4068-4074.
[6] CHEN P, SUN Z Q, BING L D, et al. Recurrent attention network on memory for aspect sentiment analysis[C]//Proceedings of Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA:Association for Computational Linguistics, 2017:452-461.
[7] FAN F F, FENG Y S, ZHAO D Y. Multi-grained attention network for aspect-level sentiment classification[C]//Proceedings of Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA:Association for Computational Linguistics, 2018:3433-3442.
[8] KIPF T N, WELLING M. Semi-supervised classification with graph convolutional networks[EB/OL].[2023-05-08]. https://arxiv.org/pdf/1609.02907.pdf.
[9] 孔博, 韩虎, 陈景景, 等. 基于虚拟依存关系与知识增强的方面级情感分析[J].计算机工程,2023,49(10):53-63. KONG B, HAN H, CHEN J J, et al. Aspect-based sentiment analysis based on virtual dependency and knowledge enhancement[J]. Computer Engineering, 2023,49(10):53-63.(in Chinese)
[10] ZHANG C, LI Q C, SONG D W. Aspect-based sentiment classification with aspect-specific graph convolutional networks[C]//Proceedings of Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Stroudsburg, USA:Association for Computational Linguistics, 2019:4568-4578.
[11] HUANG B X, CARLEY K. Syntax-aware aspect level sentiment classification with graph attention networks[C]//Proceedings of Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Stroudsburg, USA:Association for Computational Linguistics, 2019:5469-5477.
[12] TANG H, JI D H, LI C L, et al. Dependency graph enhanced dual-transformer structure for aspect-based sentiment classification[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, USA:Association for Computational Linguistics, 2020:6578-6588.
[13] LI R F, CHEN H, FENG F X, et al. Dual graph convolutional networks for aspect-based sentiment analysis[C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing. Stroudsburg, USA:Association for Computational Linguistics, 2021:6319-6329.
[14] 王汝言, 陶中原, 赵容剑, 等. 多交互图卷积网络用于方面情感分析[J]. 电子与信息学报, 2022, 44(3):1111-1118. WANG R Y, TAO Z Y, ZHAO R J, et al. Multi-interaction graph convolutional networks for aspect-level sentiment analysis[J]. Journal of Electronics & Information Technology, 2022, 44(3):1111-1118.(in Chinese)
[15] ZHANG M, QIAN T Y. Convolution over hierarchical syntactic and lexical graphs for aspect level sentiment analysis[C]//Proceedings of Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA:Association for Computational Linguistics, 2020:3540-3549.
[16] CHEN C H, TENG Z Y, ZHANG Y. Inducing target-specific latent structures for aspect sentiment classification[C]//Proceedings of Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA:Association for Computational Linguistics, 2020:5596-5607.
[17] 巫浩盛, 缪裕青, 张万桢, 等. 基于距离与图卷积网络的方面级情感分析[J]. 计算机应用研究, 2021, 38(11):3274-3278, 3321. WU H S, MIAO Y Q, ZHANG W Z, et al. Aspect level sentiment analysis based on distance and graph convolution network[J]. Application Research of Computers, 2021, 38(11):3274-3278, 3321.(in Chinese)
[18] XU G T, LIU P Y, ZHU Z F, et al. Attention-enhanced graph convolutional networks for aspect-based sentiment classification with multi-head attention[J]. Applied Sciences, 2021, 11(8):3640.
[19] 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.
[20] 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.
[21] ZHANG Z X, MA Z H, CAI S H, et al. Knowledge-enhanced dual-channel GCN for aspect-based sentiment analysis[J]. Mathematics, 2022, 10(22):4273.
[22] PENNINGTON J, SOCHER R, MANNING C. GloVe:global vectors for word representation[C]//Proceedings of Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA:Association for Computational Linguistics, 2014:1532-1543.
[23] ZHENG Y Q, LI X, NIE J Y. Store, share and transfer:learning and updating sentiment knowledge for aspect-based sentiment analysis[J]. Information Sciences:an International Journal, 2023, 635(C):151-168.
[24] HU M Q, LIU B. Mining and summarizing customer reviews[C]//Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA:ACM Press, 2004:168-177.
[25] 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. Stroudsburg, USA:Association for Computational Linguistics, 2014:27-35.
[26] 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. Stroudsburg, USA:Association for Computational Linguistics, 2014:49-54.

选择文件类型/文献管理软件名称

选择包含的内容