[1]段文杰,邓金科,张顺香,等.基于多层次知识增强的方面级情感分析模型[J].智能系统学报,2024,19(5):1287-1297.[doi:10.11992/tis.202308044]
DUAN Wenjie,DENG Jinke,ZHANG Shunxiang,et al.Aspect-based sentiment analysis model based on multilevel knowledge enhancement[J].CAAI Transactions on Intelligent Systems,2024,19(5):1287-1297.[doi:10.11992/tis.202308044]
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DUAN Wenjie,DENG Jinke,ZHANG Shunxiang,et al.Aspect-based sentiment analysis model based on multilevel knowledge enhancement[J].CAAI Transactions on Intelligent Systems,2024,19(5):1287-1297.[doi:10.11992/tis.202308044]
基于多层次知识增强的方面级情感分析模型
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
19
期数:
2024年第5期
页码:
1287-1297
栏目:
学术论文—人工智能基础
出版日期:
2024-09-05
- Title:
- Aspect-based sentiment analysis model based on multilevel knowledge enhancement
- Keywords:
- aspect-based sentiment analysis; knowledge enhancement; sentiment knowledge; syntax knowledge; conceptual knowledge; dependency graph; graph convolution network; interactive attention mechanism
- 分类号:
- TP391
- 文献标志码:
- 2024-03-21
- 摘要:
- 在方面级情感分析任务中，现有研究侧重于挖掘评论语句的语义信息和句法依赖约束，未能综合考虑情感知识、概念知识和单词之间的句法依赖类型对方面情感倾向判别准确性的影响。针对这一问题，提出一种基于多层次知识增强的方面级情感分析模型(multilevel knowledge enhancement，MLKE)，利用外部知识对评论语句进行情感、句法和概念3个层次的知识增强。首先，利用情感知识及单词之间的依赖类型来增强句子的依赖图，并通过图卷积网络建模节点特征，得到情感和句法增强的特定方面表征；其次，利用概念图谱对方面词概念增强后，与特定方面表征进行融合，得到多层次知识增强的方面表征；最后，采用交互注意力机制实现上下文表征与方面表征之间的协调优化。5个公共数据集上的实验结果表明，所提模型的准确率和宏F1值均得到提高。
- Abstract:
- In aspect-based sentiment analysis tasks, mining semantic information and syntactic dependency constraints from comment sentences is a key focus in existing research. However, this often underestimates the influence of comprehensive factors, including sentiment knowledge, conceptual knowledge, and syntactic dependency types between words on aspect sentiment orientation judgment. To address this problem, we propose an aspect-based sentiment analysis model based on multilevel knowledge enhancement (MLKE), which uses external knowledge to enhance the knowledge of comment sentences on three levels: sentiment, syntax, and concept. First, sentiment knowledge and the dependency types between words are employed to enhance the dependency graph of sentences. Specific aspect representations containing sentiment and syntactic enhancements are obtained through graph convolution networks that model modular node features. Second, to obtain multilevel knowledge-enhanced aspect representation, the concept graph is used to enhance the conceptual understanding of aspect words, and then the aspect word representation is fused with the specific aspect representation obtained in the previous step. Finally, the coordination and optimization between context representation and aspect representation is achieved using an interactive attention mechanism. The experimental results show that the accuracy and macro-F1 values of the model are improved on five datasets.
- 参考文献/References:
-
[1] 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 Zhuang, QIAN Tieyun, LI Wanli, et al. Low-resource aspect-based sentiment analysis: a survey[J]. Chinese journal of computers, 2023, 46(7): 1445-1472.
[2] 张铭泉, 周辉, 曹锦纲. 基于注意力机制的双BERT有向情感文本分类研究[J]. 智能系统学报, 2022, 17(6): 1220-1227.
ZHANG Mingquan, ZHOU Hui, CAO Jingang. Dual BERT directed sentiment text classification based on attention mechanism[J]. CAAI transactions on intelligent systems, 2022, 17(6): 1220-1227.
[3] TANG Duyu, QIN Bing, FENG Xiaocheng, et al. Effective LSTMs for target-dependent sentiment classification[EB/OL]. (2015-12-03)[2023-08-30]. http://arxiv.org/abs/1512.01100
[4] MA Dehong, LI Sujian, ZHANG Xiaodong, et al. Interactive attention networks for aspect-level sentiment classification[EB/OL]. (2017-09-04)[2023-06-25]. https://arXiv.org/abs:1709.00893.
[5] 陈景景, 韩虎, 徐学锋. 面向多方面的双通道知识增强图卷积网络模型[J/OL]. 计算机工程与科学, 2023: 1-10. (2023-04-13)[2023-06-22]. https://kns.cnki.net/kcms/detail/43.1258.tp.20230411.1325.002.html.
CHEN Jingjing, HAN Hu, XU Xuefeng. Multi aspect oriented dual channel knowledge enhanced graph convolution network model[J/OL]. Computer engineering & science, 2023: 1-10. (2023-04-13)[2023-06-25]. https://kns.cnki.net/kcms/detail/43.1258.tp.20230411.1325.002.html.
[6] ZHANG Chen, LI Qiuchi, SONG Dawei. 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. Hong Kong: Association for Computational Linguistics, 2019: 4568-4578.
[7] WANG Kai, SHEN Weizhou, YANG Yunyi, 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: Association for Computational Linguistics, 2020: 3229-3238.
[8] HOU Xiaochen, QI Peng, WANG Guangtao, et al. Graph ensemble learning over multiple dependency trees for aspect-level sentiment classification[C]//Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg: Association for Computational Linguistics, 2021: 2884-2894.
[9] 李帅, 徐彬, 韩祎珂, 等. SS-GCN: 情感增强和句法增强的方面级情感分析模型[J]. 计算机科学, 2023, 50(3): 3-11.
LI Shuai, XU Bin, HAN Yike, et al. SS-GCN: aspect-based sentiment analysis model with affective enhancement and syntactic enhancement[J]. Computer science, 2023, 50(3): 3-11.
[10] CAMBRIA E, SPEER R, HAVASI C, et al. Senticnet: A publicly available semantic resource for opinion mining[C]//AAAI fall symposium. Arlington: AAAI, 2010: 14-18.
[11] CAMBRIA E, LI Yang, XING F Z, et al. SenticNet 6: ensemble application of symbolic and subsymbolic AI for sentiment analysis[C]//Proceedings of the 29th ACM International Conference on Information & Knowledge Management. Virtual Event: ACM, 2020: 105–114.
[12] LIANG Bin, SU Hang, GUI Lin, et al. Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks[J]. Knowledge-based systems, 2022, 235: 107643.
[13] CHEN Jindong, HU Yizhou, LIU Jingping, et al. Deep short text classification with knowledge powered attention[C]//Proceedings of the AAAI conference on artificial intelligence. Honolulu: AAAI, 2019, 33(1): 6252-6259.
[14] BIAN Ximo, FENG Chong, AHMAD A, et al. Targeted sentiment classification with knowledge powered attention network[C]//2019 IEEE 31st International Conference on Tools with Artificial Intelligence. New York: IEEE, 2019: 1073-1080.
[15] WANG Yequan, HUANG Minlie, ZHU Xiaoyan, et al. Attention-based LSTM for aspect-level sentiment classification[C]//Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2016: 606-615.
[16] CHEN Peng, SUN Zhongqian, BING Lidong, et al. Recurrent attention network on memory for aspect sentiment analysis[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Copenhagen: Association for Computational Linguistics, 2017: 452-461.
[17] FAN Feifan, FENG Yansong, ZHAO Dongyan. Multi-grained attention network for aspect-level sentiment classification[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Brussels: Association for Computational Linguistics, 2018: 3433-3442.
[18] SUN Kai, ZHANG Richong, MENSAH S, et al. Aspect-level sentiment analysis via convolution over dependency tree[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Hong Kong: Association for Computational Linguistics, 2019: 5679-5688.
[19] XING F Z, PALLUCCHINI F, CAMBRIA E. Cognitive-inspired domain adaptation of sentiment lexicons[J]. Information processing and management: an international journal, 2019, 56(3): 554-564.
[20] MA Yukun, PENG Haiyun, KHAN T, et al. Sentic LSTM: a hybrid network for targeted aspect-based sentiment analysis[J]. Cognitive computation, 2018, 10(4): 639-650.
[21] YANG Qian, KADEER Z, GU Wenxia, et al. Affective knowledge augmented interactive graph convolutional network for chinese-oriented aspect-based sentiment analysis[J]. IEEE access, 2022, 10: 130686-130698.
[22] HU Linmei, YANG Tianchi, SHI Chuan, et al. Heterogeneous graph attention networks for semi-supervised short text classification[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Hong Kong: Association for Computational Linguistics, 2019: 4821-4830.
[23] JI Lei, WANG Yujing, SHI Botian, et al. Microsoft concept graph: mining semantic concepts for short text understanding[J]. Data intelligence, 2019, 1(3): 238-270.
[24] ZHU Zhenfang, ZHANG Dianyuan, LI Lin, et al. Knowledge-guided multi-granularity GCN for ABSA[J]. Information processing & management, 2023, 60(2): 103223.
[25] WANG Xiting, LIU Kunpeng, WANG Dongjie, et al. Multi-level recommendation reasoning over knowledge graphs with reinforcement learning[C]//Proceedings of the ACM Web Conference 2022. Lyon: ACM, 2022: 2098–2108.
[26] 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. Doha: Association for Computational Linguistics, 2014: 1532-1543.
[27] ZHOU Peng, SHI Wei, TIAN Jun, et al. Attention-based bidirectional long short-term memory networks for relation classification[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Berlin: Association for Computational Linguistics, 2016: 207-212.
[28] QI Peng, ZHANG Yuhao, ZHANG Yuhui, et al. Stanza: a python natural language processing toolkit for many human languages[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations. Stroudsburg: Association for Computational Linguistics, 2020: 101-108.
[29] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach: ACM, 2017: 6000–6010.
[30] DONG Li, WEI Furu, TAN Chuanqi, 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). Baltimore: Association for Computational Linguistics, 2014: 49-54.
[31] 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). Dublin: Association for Computational Linguistics, 2014: 27-35.
[32] PONTIKI M, GALANIS D, PAPAGEORGIOU H, et al. SemEval-2015 task 12: aspect based sentiment analysis[C]//Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015). Denver: Association for Computational Linguistics, 2015: 486-495.
[33] PONTIKI M, GALANIS D, PAPAGEORGIOU H, et al. SemEval-2016 task 5: aspect based sentiment analysis[C]//Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016). San Diego: Association for Computational Linguistics, 2016: 19-30.
[34] LI Xin, BING Lidong, LAM W, et al. Transformation networks for target-oriented sentiment classification[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. Melbourne: Association for Computational Linguistics, 2018: 946-956.
[35] XU Guangtao, LIU Peiyu, ZHU Zhenfang, et al. Attention-enhanced graph convolutional networks for aspect-based sentiment classification with multi-head attention[J]. Applied sciences, 2021, 11(8): 3640.
[36] WU Sixing, XU Yuanfan, WU Fangzhao, et al. Aspect-based sentiment analysis via fusing multiple sources of textual knowledge[J]. Knowledge-based systems, 2019, 183: 104868.
[37] ZHANG Mi, QIAN Tieyun. Convolution over hierarchical syntactic and lexical graphs for aspect level sentiment analysis[C]//Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2020: 3540-3549.
[38] ZHOU Jie, 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.
[39] CHEN Chenhua, TENG Zhiyang, ZHANG Yue. Inducing target-specific latent structures for aspect sentiment classification[C]//Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: Association for Computational Linguistics, 2020: 5596-5607.
[40] 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 Ruyan, TAO Zhongyuan, ZHAO Rongjian, et al. Multi-interaction graph convolutional networks for aspect-level sentiment analysis[J]. Journal of electronics & information technology, 2022, 44(3): 1111-1118.
备注/Memo
收稿日期:2023-8-30。
基金项目:国家自然科学基金面上项目（62076006）；认知智能全国重点实验室开放课题（COGOS-2023HE02）；安徽高校协同创新项目（GXXT-2021-008）.
作者简介:段文杰，硕士研究生，主要研究方向为情感分析、信息抽取、数据挖掘。E-mail：2536521292@qq.com;邓金科，硕士研究生，主要研究方向为情感分析、数据挖掘。E-mail：917566821@qq.com;张顺香，教授，博士生导师，博士，主要研究方向为智能信息处理、情感计算、复杂网络分析。主持国家级科研项目2项、省部级科研项目6项，发表学术论文80余篇。E-mail：sxzhang@aust.edu.cn。
通讯作者:张顺香. E-mail：sxzhang@aust.edu.cn
更新日期/Last Update:
2024-09-05