| Citation: | HAN H，HAO J，ZHANG Q K，et al. Multi-source knowledge fusion model for aspect-based sentiment analysis[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（9）：2688-2695 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0728
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Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task that aims to give the corresponding sentiment polarity for specific aspects that appear in review statements. Most existing aspect-based sentiment analysis methods relying on deep learning focus on mining the semantics and syntax of review statements, often ignoring the conceptual knowledge and sentiment degree information that may be involved in the review statements. To address this problem, a neural network model incorporating multi-source knowledge. It reveals the structural framework of sentences through syntactic dependencies, captures semantic connections between words through word co-occurrence, and embeds emotional networks and concept graphs to provide emotional and background knowledge for the model, and coordinated optimization of the contextual and evaluative aspects of review statements was realized through a dual-interaction attention model. Experimental results on four public datasets show that the model achieves better performance than existing models, with accuracy reaching 75.00%, 77.90%, 81.55%, and 90.10%, respectively, all of which were improved compared to the benchmark model. This achievement not only verifies the effectiveness of multi-source knowledge fusion in ABSA tasks, but also provides new ideas and methods for future research.
| [1] |
LIU B, ZHANG L. A survey of opinion mining and sentiment analysis[M]//AGGARWAL C C, ZHAI C X. Mining text data. Berlin: Springer, 2012: 415-463.
|
| [2] |
唐晓波, 刘广超. 细粒度情感分析研究综述[J]. 图书情报工作, 2017, 61(5): 132-140.
TANG X B, LIU G C. Research review on fine-grained sentiment analysis[J]. Library and Information Service, 2017, 61(5): 132-140(in Chinese).
|
| [3] |
KIM Y. Convolutional neural networks for sentence classification[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg: ACL, 2014: 1746-1751.
|
| [4] |
TANG D Y, QIN B, FENG X C. Effective LSTMs for target-dependent sentiment classification[C]//Proceedings of the 26th International Conference on Computational Linguistics. Stroudsburg: ACL, 2016: 3298-3307.
|
| [5] |
ZHANG C, LI Q C, SONG D W. Aspect-based sentiment classification with aspect-specific graph convolutional networks[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing. Stroudsburg: ACL, 2019: 4567-4577.
|
| [6] |
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.
|
| [7] |
谢珺, 王雨竹, 陈波, 等. 基于双指导注意力网络的属性情感分析模型[J]. 计算机研究与发展, 2022, 59(12): 2831-2843. doi: 10.7544/issn1000-1239.20210708
XIE J, WANG Y Z, CHEN B, et al. Aspect-based sentiment analysis model with bi-guide attention network[J]. Journal of Computer Research and Development, 2022, 59(12): 2831-2843(in Chinese). doi: 10.7544/issn1000-1239.20210708
|
| [8] |
ZHANG M, QIAN T Y. Convolution over hierarchical syntactic and lexical graphs for aspect level sentiment analysis[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg: ACL, 2020: 3540-3549.
|
| [9] |
MA Y K, PENG H Y, CAMBRIA E. Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM[C]//Proceedings of the 32nd Conference on Artificial Intelligence. Palo Alto: AAAI, 2018: 5876-5883.
|
| [10] |
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. doi: 10.1016/j.knosys.2020.106292
|
| [11] |
ZHAO A P, YU Y. Knowledge-enabled BERT for aspect-based sentiment analysis[J]. Knowledge-Based Systems, 2021, 227: 107220. doi: 10.1016/j.knosys.2021.107220
|
| [12] |
WANG Y Q, HUANG M L, ZHU X Y, et al. Attention-based LSTM for aspect-level sentiment classification[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg: ACL, 2016: 606-615.
|
| [13] |
MA D H, LI S J, ZHANG X D, et al. Interactive attention networks for aspect-level sentiment classification[C]//Proceedings of the 26th International Joint Conference on Artificial Intelligence. New York: ACM, 2017: 4068-4074.
|
| [14] |
PANG S G, XUE Y, YAN Z H, et al. Dynamic and multi-channel graph convolutional networks for aspect-based sentiment analysis[C]//Proceedings of the Findings of the Association for Computational Linguistics. Stroudsburg: ACL, 2021: 2627-2636.
|
| [15] |
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: ACL, 2021: 6319-6329.
|
| [16] |
ZHONG Q H, DING L, LIU J H, et al. Knowledge graph augmented network towards multiview representation learning for aspect-based sentiment analysis[EB/OL]. (2022-01-13)[2022-08-01]. http://arxiv.org/abs/2201.04831.
|
| [17] |
CHEN J D, HU Y Z, LIU J P, et al. Deep short text classification with knowledge powered attention[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2019, 33(1): 6252-6259. doi: 10.1609/aaai.v33i01.33016252
|
| [18] |
BIAN X M, FENG C, AHMAD A, et al. Targeted sentiment classification with knowledge powered attention network[C]//Prroceedings of the IEEE 31st International Conference on Tools with Artificial Intelligence. Piscataway: IEEE Press, 2019: 1073-1080.
|
| [19] |
CAMBRIA E, SPEER R, HAVASI C, et al. SenticNet: A publicly available semantic resource for opinion mining[C]//Proceedings of the AAAI Fall Symposium. Palo Alto: AAAI, 2010: 14-18.
|
| [20] |
CAMBRIA E, LI Y, 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. New York: ACM, 2020: 105-114.
|
| [21] |
XING F Z, PALLUCCHINI F, CAMBRIA E. Cognitive-inspired domain adaptation of sentiment lexicons[J]. Information Processing and Management, 2019, 56(3): 554-564. doi: 10.1016/j.ipm.2018.11.002
|
| [22] |
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. doi: 10.1016/j.knosys.2021.107643
|
| [23] |
PENNINGTON J, SOCHER R, MANNING C. GloVe: Global vectors for word representation[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg: ACL, 2014: 1532-1543.
|
| [24] |
JI L, WANG Y J, SHI B T, et al. Microsoft concept graph: Mining semantic concepts for short text understanding[J]. Data Intelligence, 2019, 1(3): 238-270. doi: 10.1162/dint_a_00013
|
| [25] |
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: ACL, 2014: 49-54.
|
| [26] |
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: ACL, 2014: 27-35.
|
| [27] |
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. Stroudsburg: ACL, 2015: 486-495.
|
| [28] |
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. Stroudsburg: ACL, 2016: 19-30.
|
| [29] |
CHEN P, SUN Z Q, BING L D, et al. Recurrent attention network on memory for aspect sentiment analysis[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg: ACL, 2017: 452-461.
|
| [30] |
FAN F F, FENG Y S, ZHAO D Y. Multi-grained attention network for aspect-level sentiment classification[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg: ACL, 2018: 3433-3442.
|
| [31] |
HUANG B X, OU Y L, CARLEY K M. Aspect level sentiment classification with attention-over-attention neural networks[M]// THOMSON R, DANCY C, HYDER A, et al. Social, cultural, and behavioral modeling. Berlin: Springer, 2018: 197-206.
|
| [32] |
ZHU X F, ZHU L, GUO J F, et al. GL-GCN: Global and local dependency guided graph convolutional networks for aspect-based sentiment classification[J]. Expert Systems with Applications, 2021, 186: 115712. doi: 10.1016/j.eswa.2021.115712
|
| [33] |
韩虎, 郝俊, 张千锟, 等. 知识增强的交互注意力方面级情感分析模型[J]. 计算机科学与探索, 2023, 17(3): 709-718. doi: 10.3778/j.issn.1673-9418.2108082
HAN H, HAO J, ZHANG Q K, et al. Knowledge-enhanced interactive attention model for aspect-based sentiment analysis[J]. Journal of Frontiers of Computer Science and Technology, 2023, 17(3): 709-718(in Chinese). doi: 10.3778/j.issn.1673-9418.2108082
|
| [34] |
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. doi: 10.11999/JEIT210459
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). doi: 10.11999/JEIT210459
|
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