Top

World Wide Web

Published in:

17-07-2023

Aspect sentiment quadruple extraction based on the sentence-guided grid tagging scheme

Authors: Linan Zhu, Yinwei Bao, Minhao Xu, Jianxin Li, Zhechao Zhu, Xiangjie Kong

Published in: World Wide Web | Issue 5/2023

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

The aspect sentiment quadruple extraction (ASQE) task aims to extract all the opinion quadruples (aspect term, aspect category, opinion term, sentiment polarity) from the review text. However, there are often implicit expressions in the review text, and implicit opinion quadruples are difficult to be represented and extracted. To this end, we propose a novel end-to-end approach for the explicit and implicit ASQE tasks. Specifically, we use a multi-scale convolutional neural network (MS-CNN) and bidirectional long short-term memory neural network (BiLSTM) to capture the local features and contextual features. Then we design a novel sentence-guided grid tagging scheme to extract explicit and implicit opinion quadruples contained in the reviews, in which the grid of the representation of the sentence’s overall meaning is used to mark the implicit expression. Extensive experimental results indicate that our model outperforms strong baselines significantly and achieves state-of-the-art performance.

previous article TFPA: A traceable federated privacy aggregation protocol

next article Efficiently computing alternative paths in game maps

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Zhu, L., Xu, M., Xu, Y., Zhu, Z., Zhao, Y., Kong, X.: A multi-attribute decision making approach based on information extraction for real estate buyer profiling. World Wide Web, 1–19 (2022). https://doi.org/10.1007/s11280-022-01010-9

Thet, T.T., Na, J.-C., Khoo, C.S.: Aspect-based sentiment analysis of movie reviews on discussion boards. J. Inf. Sci. 36(6), 82–848 (2010). https://doi.org/10.1177/0165551510388123CrossRef

Xie, S., Cao, J., Wu, Z., Liu, K., Tao, X., Xie, H.: Sentiment analysis of chinese e-commerce reviews based on bert. In: 2020 IEEE 18th International Conference on Industrial Informatics (INDIN), vol. 1, pp. 71–718 (2020). https://doi.org/10.1109/INDIN45582.2020.9442190. IEEE

Yadav, V., Verma, P., Katiyar, V.: E-commerce product reviews using aspect based hindi sentiment analysis. In: 2021 International Conference on Computer Communication and Informatics (ICCCI), pp. 1–8 (2021). https://doi.org/10.1109/ICCCI50826.2021.9402365. IEEE

Chandra, J.K., Cambria, E., Nanetti, A.: One belt, one road, one sentiment? a hybrid approach to gauging public opinions on the new silk road initiative. In: 2020 International Conference on Data Mining Workshops (ICDMW), pp. 7–14 (2020). https://doi.org/10.1109/ICDMW51313.2020.00011. IEEE

Shuang, K., Yang, Q., Loo, J., Li, R., Gu, M.: Feature distillation network for aspect-based sentiment analysis. Inform. Fus. 61, 13–23 (2020). https://doi.org/10.1016/j.inffus.2020.03.003CrossRef

Zhao, A., Yu, Y.: Knowledge-enabled bert for aspect-based sentiment analysis. Knowledge-Based Systems 227, 107220 (2021). https://doi.org/10.1016/j.knosys.2021.107220CrossRef

Ren, Z.-M., Zheng, Y., Du, W.-L., Pan, X.: A joint model for extracting latent aspects and their ratings from online employee reviews. Front. Phys. 311 (2022). https://doi.org/10.3389/fphy.2022.822351

Zhao, H., Huang, L., Zhang, R., Lu, Q., Xue, H.: SpanMlt: A spanbased multi-task learning framework for pair-wise aspect and opinion terms extraction. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3239–3248. Association for Computational Linguistics, Online (2020).https://doi.org/10.18653/v1/2020.acl-main.296

10.

Chen, S., Liu, J., Wang, Y., Zhang, W., Chi, Z.: Synchronous doublechannel recurrent network for aspect-opinion pair extraction. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 6515–6524. Association for Computational Linguistics, Online (2020).https://doi.org/10.18653/v1/2020.acl-main.582

11.

Gao, L., Wang, Y., Liu, T., Wang, J., Zhang, L., Liao, J.: Question-driven span labeling model for aspec-opinion pair extraction. Proc. AAAI Conf. Artif. Intell. 35(14), 12875–12883 (2021). https://doi.org/10.1609/aaai.v35i14.17523CrossRef

12.

Wu, S., Fei, H., Ren, Y., Ji, D., Li, J.: Learn from syntax: Improving pairwise aspect and opinion terms extractionwith rich syntactic knowledge. arXiv:2105.02520 (2021). https://doi.org/10.48550/arXiv2105.02520

13.

Schmitt, M., Steinheber, S., Schreiber, K., Roth, B.: Joint aspect and polarity classification for aspect-based sentiment analysis with end-to-end neural networks. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 1109–1114. Association for Computational Linguistics, Brussels, Belgium (2018). https://doi.org/10.18653/v1/D18-1139

14.

Cai, H., Tu, Y., Zhou, X., Yu, J., Xia, R.: Aspect-category based sentiment analysis with hierarchical graph convolutional network. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 833–843. International Committee on Computational Linguistics, Barcelona, Spain (Online) (2020).https://doi.org/10.18653/v1/2020.coling-main.72

15.

Liu, J., Teng, Z., Cui, L., Liu, H., Zhang, Y.: Solving aspect category sentiment analysis as a text generation task. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 4406–4416. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic (2021).https://doi.org/10.18653/v1/2021.emnlp-main.361

16.

Peng, H., Xu, L., Bing, L., Huang, F., Lu, W., Si, L.: Knowing what, how and why: A near complete solution for aspect-based sentiment analysis. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 8600–8607 (2020). https://doi.org/10.1609/aaai.v34i05.6383

17.

Chen, Z., Qian, T.: Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685–3694. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.acl-main.340

18.

Zhang, C., Li, Q., Song, D., Wang, B.: A multi-task learning framework for opinion triplet extraction. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 819–828. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.findings-emnlp.72

19.

Fei, H., Ren, Y., Zhang, Y., Ji, D.: Nonautoregressive encoder-decoder neural framework for end-to-end aspect-based sentiment triplet extraction. IEEE Transactions on Neural Networks and Learning Systems (2021). https://doi.org/10.1109/TNNLS.2021.3129483CrossRef

20.

Zhang, W., Li, X., Deng, Y., Bing, L., Lam, W.: Towards generative aspect-based sentiment analysis. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pp. 504–510. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-short.64

21.

Chen, Z., Huang, H., Liu, B., Shi, X., Jin, H.: Semantic and syntactic enhanced aspect sentiment triplet extraction. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 1474–1483. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.findings-acl.128

22.

Jiang, Q., Chen, L., Xu, R., Ao, X., Yang, M.: A challenge dataset and effective models for aspect-based sentiment analysis. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 6280–6285. Association for Computational Linguistics, Hong Kong, China (2019). https://doi.org/10.18653/v1/D19-1654

23.

Zhu, P., Chen, Z., Zheng, H., Qian, T.: Aspect aware learning for aspect category sentiment analysis. ACM Trans. Knowl. Discov. Data (TKDD) 13(6), 1–21 (2019). https://doi.org/10.1145/3350487CrossRef

24.

Li, Y., Yin, C., Zhong, S.-h., Pan, X.: Multi-instance multi-label learning networks for aspect-category sentiment analysis. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 3550–3560. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.287

25.

Li, Y., Yin, C., Zhong, S.-h.: Sentence constituent-aware aspect-category sentiment analysis with graph attention networks. In: CCF International Conference on Natural Language Processing and Chinese Computing, Springer pp. 815–827 (2020). https://doi.org/10.1007/978-3-030-60450-9_64

26.

Cai, H., Xia, R., Yu, J.: Aspect-category-opinion-sentiment quadruple extraction with implicit aspects and opinions. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 340–350. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.29

27.

Zhang, W., Deng, Y., Li, X., Yuan, Y., Bing, L., Lam, W.: Aspect sentiment quad prediction as paraphrase generation. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 9209–9219. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic (2021). https://doi.org/10.18653/v1/2021.emnlp-main.726

28.

Fei, H., Li, F., Li, C., Wu, S., Li, J., Ji, D.: Inheriting the wisdom of predecessors: A multiplex cascade framework for unified aspect-based sentiment analysis. In: Raedt, L.D. (ed.) Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22, pp. 4121–4128. International Joint Conferences on Artificial Intelligence Organization, (2022). https://doi.org/10.24963/ijcai.2022/572. MainTrack

29.

Yan, H., Dai, J., Ji, T., Qiu, X., Zhang, Z.: A unified generative framework for aspect-based sentiment analysis. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 2416–2429. Association for Computational Linguistics, Online (2021). https://doi.org/10.18653/v1/2021.acl-long.188

30.

Cai, Z., Fan, Q., Feris, R.S., Vasconcelos, N.: A unified multi-scale deep convolutional neural network for fast object detection. In: European Conference on Computer Vision, pp. 354–370 (2016). https://doi.org/10.1007/978-3-319-46493-0_22. Springer

31.

Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional lstm and other neural network architectures. Neural Networks 18(5–6), 602–610 (2005). https://doi.org/10.1016/j.neunet.2005.06.042CrossRef

32.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. Adv. Neural. Inform. Proc. Syst. 30 (2017). https://doi.org/10.48550/arXiv.1706.03762

33.

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015). https://doi.org/10.1109/CVPR.2015.7298594

34.

Wu, Z., Ying, C., Zhao, F., Fan, Z., Dai, X., Xia, R.: Grid tagging scheme for aspect-oriented fine-grained opinion extraction. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 2576–2585. Association for Computational Linguistics, Online (2020).https://doi.org/10.18653/v1/2020.findings-emnlp.234

35.

Turney, P.: Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 417–424. Association for Computational Linguistics, Philadelphia, Pennsylvania, USA (2002). https://doi.org/10.3115/1073083.1073153

36.

Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? sentiment classification using machine learning techniques. In: Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP 2002), pp. 79–86. Association for Computational Linguistics, (2002). https://doi.org/10.3115/1118693.1118704

37.

Yu, H., Hatzivassiloglou, V.: Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences. In: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, pp. 129–136 (2003).https://aclanthology.org/W03-1017

38.

Li, B., Fei, H., Wu, Y., Zhang, J., Wu, S., Li, J., Liu, Y., Liao, L., Chua, T.-S., Li, F., et al.: Diaasq: A benchmark of conversational aspect-based sentiment quadruple analysis. arXiv:2211.05705 (2022). https://doi.org/10.48550/arXiv.2211.05705

39.

Areed, S., Alqaryouti, O., Siyam, B., Shaalan, K.: Aspect-based sentiment analysis for arabic government reviews. In: Recent Advances in NLP: the Case of Arabic Language, pp. 143–162. Springer, (2020). https://doi.org/10.1007/978-3-030-34614-0_8

40.

Eldin, S.S., Mohammed, A., Eldin, A.S., Hefny, H.: An enhanced opinion retrieval approach via implicit feature identification. Journal of Intelligent Information Systems 57(1), 101–126 (2021). https://doi.org/10.1007/s10844-020-00622-9CrossRef

41.

Lazhar, F., Yamina, T.-G.: Mining explicit and implicit opinions from reviews. Int. J Data Mining Modell. Manag. 8(1), 77–92 (2016). https://doi.org/10.1504/IJDMMM.2016.075966CrossRef

42.

Luo, H., Ji, L., Li, T., Jiang, D., Duan, N.: GRACE: Gradient harmonized and cascaded labeling for aspect-based sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 54–64. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.findings-emnlp.6

43.

Liang, Y., Meng, F., Zhang, J., Chen, Y., Xu, J., Zhou, J.: An iterative multi-knowledge transfer network for aspect-based sentiment analysis. In: Findings of the Association for Computational Linguistics: EMNLP 2021, pp. 1768–1780. Association for Computational Linguistics, Punta Cana, Dominican Republic (2021). https://doi.org/10.18653/v1/2021.findings-emnlp.152

44.

Chen, P., Chen, S., Liu, J.: Hierarchical sequence labeling model for aspect sentiment triplet extraction. In: CCF International Conference on Natural Language Processing and Chinese Computing, pp. 654–666 (2020) Springer. https://doi.org/10.1007/978-3-030-60450-9_52

45.

Xu, L., Li, H., Lu, W., Bing, L.: Position-aware tagging for aspect sentiment triplet extraction. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 2339–2349. Association for Computational Linguistics, Online (2020). https://doi.org/10.18653/v1/2020.emnlp-main.183

46.

Zamil, A.A.A., Hasan, S., Baki, S.M.J., Adam, J.M., Zaman, I.: Emotion detection from speech signals using voting mechanism on classified frames. In: 2019 International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), pp. 281–285 (2019). https://doi.org/10.1109/ICREST.2019.8644168. IEEE

47.

Pennington, J., Socher, R., Manning, C.: GloVe: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543. Association for Computational Linguistics, Doha, Qatar (2014). https://doi.org/10.3115/v1/D14-1162

48.

Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. (2014) arXiv:1412.6980

49.

Qiu, G., Liu, B., Bu, J., Chen, C.: Opinion word expansion and target extraction through double propagation. Comput. Linguistics 37(1), 9–27 (2011). https://doi.org/10.1162/coli_a_00034CrossRef

50.

Wan, H., Yang, Y., Du, J., Liu, Y., Qi, K., Pan, J.Z.: Target-aspectsentiment joint detection for aspect-based sentiment analysis. In: Proc AAAI Conf. Artif. Intell, vol. 34, 9122–9129 (2020). https://doi.org/10.1609/aaai.v34i05.6447

51.

Wang, W., Pan, S.J., Dahlmeier, D., Xiao, X.: Coupled multi-layer attentions for co-extraction of aspect and opinion terms. In: Proc AAAI Conf Artif Intell, vol. 31 (2017). https://doi.org/10.1609/aaai.v31i1.10974

Title: Aspect sentiment quadruple extraction based on the sentence-guided grid tagging scheme
Authors: Linan Zhu
Yinwei Bao
Minhao Xu
Jianxin Li
Zhechao Zhu
Xiangjie Kong
Publication date: 17-07-2023
Publisher: Springer US
Published in: World Wide Web / Issue 5/2023
Print ISSN: 1386-145X
Electronic ISSN: 1573-1413
DOI: https://doi.org/10.1007/s11280-023-01185-9

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Other articles of this Issue 5/2023

DIRS-KG: a KG-enhanced interactive recommender system based on deep reinforcement learning

HTSE: hierarchical time-surface model for temporal knowledge graph embedding

Knowledge-graph-enabled biomedical entity linking: a survey

Metacognition-driven user-to-project recommendation for online education services

Course map learning with graph convolutional network based on AuCM

A performant and incremental algorithm for knowledge graph entity typing

Premium Partner