Skip to main content
Top
Published in:

28-12-2022

Aspect Sentiment Triplet Extraction Incorporating Syntactic Constituency Parsing Tree and Commonsense Knowledge Graph

Authors: Zhenda Hu, Zhaoxia Wang, Yinglin Wang, Ah-Hwee Tan

Published in: Cognitive Computation | Issue 1/2023

Log in

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

search-config
loading …

Abstract

The aspect sentiment triplet extraction (ASTE) task aims to extract the target term and the opinion term, and simultaneously identify the sentiment polarity of target-opinion pairs from the given sentences. While syntactic constituency information and commonsense knowledge are both important and valuable for the ASTE task, only a few studies have explored how to integrate them via flexible graph convolutional networks (GCNs) for this task. To address this gap, this paper proposes a novel end-to-end model, namely GCN-EGTS, which is an enhanced Grid Tagging Scheme (GTS) for ASTE leveraging syntactic constituency parsing tree and a commonsense knowledge graph based on GCNs. Specifically, two types of GCNs are developed to model the information involved, namely span GCN for syntactic constituency parsing tree and relational GCN (R-GCN) for commonsense knowledge graph. In addition, a new loss function is designed by incorporating several constraints for GTS to enhance the original tagging scheme. The extensive experiments on several public datasets demonstrate that GCN-EGTS outperforms the state-of-the-art approaches significantly for the ASTE task based on the evaluation metrics. The outcomes of this research indicate that effectively incorporating syntactic constituency parsing information and commonsense knowledge is a promising direction for the ASTE task.

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

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!

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!

Literature
1.
go back to reference Pontiki M, Galanis D, Papageorgiou H, Androutsopoulos I, Manandhar S, Al-Smadi M, Al-Ayyoub M, Zhao Y, Qin B, DeClercq O. Semeval-2016 task 5: aspect based sentiment analysis. In: International Workshop on Semantic Evaluation. 2016;19–30. Pontiki M, Galanis D, Papageorgiou H, Androutsopoulos I, Manandhar S, Al-Smadi M, Al-Ayyoub M, Zhao Y, Qin B, DeClercq O. Semeval-2016 task 5: aspect based sentiment analysis. In: International Workshop on Semantic Evaluation. 2016;19–30.
2.
go back to reference 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. 2020;34:8600–8607. 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. 2020;34:8600–8607.
3.
go back to reference 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;819–828. 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;819–828.
4.
go back to reference 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;2576–2585. 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;2576–2585.
5.
go back to reference 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). 2020;2339–2349. 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). 2020;2339–2349.
6.
go back to reference Xu L, Chia YK, Bing L. Learning span-level interactions for aspect sentiment triplet extraction. 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) 2021;4755–4766. Xu L, Chia YK, Bing L. Learning span-level interactions for aspect sentiment triplet extraction. 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) 2021;4755–4766.
7.
go back to reference 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;1474–1483. 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;1474–1483.
8.
go back to reference Zhang M, Qian T. Convolution over hierarchical syntactic and lexical graphs for aspect level sentiment analysis. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020;3540–3549. Zhang M, Qian T. Convolution over hierarchical syntactic and lexical graphs for aspect level sentiment analysis. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020;3540–3549.
9.
go back to reference Phan MH, Ogunbona PO. Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020;3211–3220. Phan MH, Ogunbona PO. Modelling context and syntactical features for aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020;3211–3220.
10.
go back to reference Gómez-Rodríguez C, Alonso-Alonso I, Vilares D. How important is syntactic parsing accuracy? An empirical evaluation on rule-based sentiment analysis. Artif Intell Rev. 2019;52(3):2081–97.CrossRef Gómez-Rodríguez C, Alonso-Alonso I, Vilares D. How important is syntactic parsing accuracy? An empirical evaluation on rule-based sentiment analysis. Artif Intell Rev. 2019;52(3):2081–97.CrossRef
11.
go back to reference Ghosal D, Hazarika D, Roy A, Majumder N, Mihalcea R, Poria S. Kingdom: Knowledge-guided domain adaptation for sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics 2020;3198–3210 Ghosal D, Hazarika D, Roy A, Majumder N, Mihalcea R, Poria S. Kingdom: Knowledge-guided domain adaptation for sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics 2020;3198–3210
12.
go back to reference Zhao A, Yu Y. Knowledge-enabled bert for aspect-based sentiment analysis. Knowl-Based Syst. 2021;227:107220. Zhao A, Yu Y. Knowledge-enabled bert for aspect-based sentiment analysis. Knowl-Based Syst. 2021;227:107220.
13.
go back to reference Mao R, Liu Q, He K, Li W, Cambria E. The biases of pre-trained language models: an empirical study on prompt-based sentiment analysis and emotion detection. IEEE Trans Affect Comput. 2022. Mao R, Liu Q, He K, Li W, Cambria E. The biases of pre-trained language models: an empirical study on prompt-based sentiment analysis and emotion detection. IEEE Trans Affect Comput. 2022.
14.
go back to reference Kumar JA, Trueman TE, Cambria E. Gender-based multi-aspect sentiment detection using multilabel learning. Inform Sci. 2022;606:453–68.CrossRef Kumar JA, Trueman TE, Cambria E. Gender-based multi-aspect sentiment detection using multilabel learning. Inform Sci. 2022;606:453–68.CrossRef
15.
go back to reference Khoo CS, Johnkhan SB. Lexicon-based sentiment analysis: Comparative evaluation of six sentiment lexicons. J Inf Sci. 2018;44(4):491–511. Khoo CS, Johnkhan SB. Lexicon-based sentiment analysis: Comparative evaluation of six sentiment lexicons. J Inf Sci. 2018;44(4):491–511.
16.
go back to reference Zhou J, Huang JX, Hu QV, He L. Sk-gcn: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowl-Based Syst. 2020;205:106292. Zhou J, Huang JX, Hu QV, He L. Sk-gcn: Modeling syntax and knowledge via graph convolutional network for aspect-level sentiment classification. Knowl-Based Syst. 2020;205:106292.
17.
go back to reference Sun K, Zhang R, Mensah S, Mao Y, Liu X. Aspect-level sentiment analysis via convolution over dependency tree. 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). 2019;5679–5688. Sun K, Zhang R, Mensah S, Mao Y, Liu X. Aspect-level sentiment analysis via convolution over dependency tree. 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). 2019;5679–5688.
18.
go back to reference Cambria E, Liu Q, Decherchi S, Xing F, Kwok K. SenticNet 7: a commonsense-based neurosymbolic AI framework for explainable sentiment analysis. Proceedings of LREC. 2022. Cambria E, Liu Q, Decherchi S, Xing F, Kwok K. SenticNet 7: a commonsense-based neurosymbolic AI framework for explainable sentiment analysis. Proceedings of LREC. 2022.
19.
go back to reference He K, Mao R, Gong T, Li C, Cambria E. Meta-based self-training and re-weighting for aspect-based sentiment analysis. IEEE Trans Affect Comput. 2022. He K, Mao R, Gong T, Li C, Cambria E. Meta-based self-training and re-weighting for aspect-based sentiment analysis. IEEE Trans Affect Comput. 2022.
20.
go back to reference Valdivia A, Luzón MV, Cambria E, Herrera F. Consensus vote models for detecting and filtering neutrality in sentiment analysis. Information Fusion. 2018;44:126–35.CrossRef Valdivia A, Luzón MV, Cambria E, Herrera F. Consensus vote models for detecting and filtering neutrality in sentiment analysis. Information Fusion. 2018;44:126–35.CrossRef
21.
go back to reference Wang Z, Ho S-B, Cambria E. Multi-level fine-scaled sentiment sensing with ambivalence handling. Int J Uncertainty Fuzziness Knowledge Based Syst. 2020;28(04):683–97.CrossRef Wang Z, Ho S-B, Cambria E. Multi-level fine-scaled sentiment sensing with ambivalence handling. Int J Uncertainty Fuzziness Knowledge Based Syst. 2020;28(04):683–97.CrossRef
22.
go back to reference Ma Y, Peng H, Cambria E. Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2018;32. Ma Y, Peng H, Cambria E. Targeted aspect-based sentiment analysis via embedding commonsense knowledge into an attentive LSTM. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2018;32.
23.
go back to reference Dragoni M, Donadello I, Cambria E. Ontosenticnet 2: Enhancing reasoning within sentiment analysis. IEEE Intelligent Systems. 2022;37(2):103–10. Dragoni M, Donadello I, Cambria E. Ontosenticnet 2: Enhancing reasoning within sentiment analysis. IEEE Intelligent Systems. 2022;37(2):103–10.
24.
go back to reference Yao L, Mao C, Luo Y. Graph convolutional networks for text classification. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2019;33:7370–7377. Yao L, Mao C, Luo Y. Graph convolutional networks for text classification. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2019;33:7370–7377.
25.
go back to reference Marcheggiani D, Bastings J, Titov I. Exploiting semantics in neural machine translation with graph convolutional networks. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2018;2(Short Papers):486–492. Marcheggiani D, Bastings J, Titov I. Exploiting semantics in neural machine translation with graph convolutional networks. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2018;2(Short Papers):486–492.
26.
go back to reference Marcheggiani D, Titov I. Encoding sentences with graph convolutional networks for semantic role labeling. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2017;1506–1515. Marcheggiani D, Titov I. Encoding sentences with graph convolutional networks for semantic role labeling. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 2017;1506–1515.
27.
go back to reference Trueman TE, Cambria E. A convolutional stacked bidirectional lstm with a multiplicative attention mechanism for aspect category and sentiment detection. Cogn Comput. 2021;13(6):1423–32. Trueman TE, Cambria E. A convolutional stacked bidirectional lstm with a multiplicative attention mechanism for aspect category and sentiment detection. Cogn Comput. 2021;13(6):1423–32.
28.
go back to reference Mao R, Li X. Bridging towers of multi-task learning with a gating mechanism for aspect-based sentiment analysis and sequential metaphor identification. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2021;35:13534–13542. Mao R, Li X. Bridging towers of multi-task learning with a gating mechanism for aspect-based sentiment analysis and sequential metaphor identification. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2021;35:13534–13542.
29.
go back to reference Balazs JA, Velásquez JD. Opinion mining and information fusion: a survey. Information Fusion. 2016;27:95–110.CrossRef Balazs JA, Velásquez JD. Opinion mining and information fusion: a survey. Information Fusion. 2016;27:95–110.CrossRef
30.
go back to reference Mohammad A-S, Hammad MM, Sa’ad A, Saja A-T, Cambria E. Gated recurrent unit with multilingual universal sentence encoder for Arabic aspect-based sentiment analysis. Knowl-Based Syst. 2021;107540. Mohammad A-S, Hammad MM, Sa’ad A, Saja A-T, Cambria E. Gated recurrent unit with multilingual universal sentence encoder for Arabic aspect-based sentiment analysis. Knowl-Based Syst. 2021;107540.
31.
go back to reference Lu G, Yu H, Xue Y, Qiu Z, Zhong W. Scan: Syntactic knowledge and commonsense knowledge adapter based network for aspect-level sentiment classification. In: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. 2021;393–399. Lu G, Yu H, Xue Y, Qiu Z, Zhong W. Scan: Syntactic knowledge and commonsense knowledge adapter based network for aspect-level sentiment classification. In: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. 2021;393–399.
32.
go back to reference Tai KS, Socher R, Manning CD. Improved semantic representations from tree-structured long short-term memory networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015;1556–1566. Tai KS, Socher R, Manning CD. Improved semantic representations from tree-structured long short-term memory networks. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015;1556–1566.
33.
go back to reference Xu H, Liu B, Shu L, Philip SY. Double embeddings and CNN-based sequence labeling for aspect extraction. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2018;592–598. Xu H, Liu B, Shu L, Philip SY. Double embeddings and CNN-based sequence labeling for aspect extraction. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). 2018;592–598.
34.
35.
go back to reference Marcheggiani D, Titov I. Graph convolutions over constituent trees for syntax-aware semantic role labeling. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020;3915–3928. Marcheggiani D, Titov I. Graph convolutions over constituent trees for syntax-aware semantic role labeling. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020;3915–3928.
36.
go back to reference Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735–80.CrossRef Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735–80.CrossRef
37.
go back to reference Kenton JDM-WC, Toutanova LK. BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT. 2019;4171–4186. Kenton JDM-WC, Toutanova LK. BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT. 2019;4171–4186.
38.
go back to reference Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I. Attention is all you need. Adv Neural Inf Proces Syst. 2017;30. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I. Attention is all you need. Adv Neural Inf Proces Syst. 2017;30.
39.
go back to reference Speer R, Chin J, Havasi C. ConceptNet 5.5: an open multilingual graph of general knowledge. In: Thirty-first AAAI Conference on Artificial Intelligence. 2017. Speer R, Chin J, Havasi C. ConceptNet 5.5: an open multilingual graph of general knowledge. In: Thirty-first AAAI Conference on Artificial Intelligence. 2017.
40.
go back to reference Schlichtkrull M, Kipf TN, Bloem P, Berg Rvd, Titov I, Welling M. Modeling relational data with graph convolutional networks. In: European Semantic Web Conference. 2018;593–607. Springer. Schlichtkrull M, Kipf TN, Bloem P, Berg Rvd, Titov I, Welling M. Modeling relational data with graph convolutional networks. In: European Semantic Web Conference. 2018;593–607. Springer.
41.
go back to reference Yang B, Yih SW-t, He X, Gao J, Deng L. Embedding entities and relations for learning and inference in knowledge bases. In: Proceedings of the International Conference on Learning Representations (ICLR). 2015. Yang B, Yih SW-t, He X, Gao J, Deng L. Embedding entities and relations for learning and inference in knowledge bases. In: Proceedings of the International Conference on Learning Representations (ICLR). 2015.
42.
go back to reference Pontiki M, Galanis D, Papageorgiou H, Manandhar S, Androutsopoulos I. Semeval-2015 task 12: aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015) 2015;486–495. Pontiki M, Galanis D, Papageorgiou H, Manandhar S, Androutsopoulos I. Semeval-2015 task 12: aspect based sentiment analysis. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015) 2015;486–495.
43.
go back to reference Kirange D, Deshmukh RR, Kirange M. Aspect based sentiment analysis semeval-2014 task 4. Asian J Comput Sci Inf Technol (AJCSIT). 2014;4:72–75. Kirange D, Deshmukh RR, Kirange M. Aspect based sentiment analysis semeval-2014 task 4. Asian J Comput Sci Inf Technol (AJCSIT). 2014;4:72–75.
44.
go back to reference Kingma DP, Ba J. Adam: a method for stochastic optimization. In: ICLR (Poster). 2015. Kingma DP, Ba J. Adam: a method for stochastic optimization. In: ICLR (Poster). 2015.
45.
go back to reference He R, Lee WS, Ng HT, Dahlmeier D. An interactive multi-task learning network for end-to-end aspect-based sentiment analysis. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019;504–515. He R, Lee WS, Ng HT, Dahlmeier D. An interactive multi-task learning network for end-to-end aspect-based sentiment analysis. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019;504–515.
46.
go back to reference Fan Z, Wu Z, Dai X, Huang S, Chen J. Target-oriented opinion words extraction with target-fused neural sequence labeling. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019;2509–2518. Fan Z, Wu Z, Dai X, Huang S, Chen J. Target-oriented opinion words extraction with target-fused neural sequence labeling. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019;2509–2518.
Metadata
Title
Aspect Sentiment Triplet Extraction Incorporating Syntactic Constituency Parsing Tree and Commonsense Knowledge Graph
Authors
Zhenda Hu
Zhaoxia Wang
Yinglin Wang
Ah-Hwee Tan
Publication date
28-12-2022
Publisher
Springer US
Published in
Cognitive Computation / Issue 1/2023
Print ISSN: 1866-9956
Electronic ISSN: 1866-9964
DOI
https://doi.org/10.1007/s12559-022-10078-4

Other articles of this Issue 1/2023

Cognitive Computation 1/2023 Go to the issue

Premium Partner