Skip to main content

Advertisement

Springer Nature Link
Log in

A Hybrid Semantics and Syntax-Based Graph Convolutional Network for Aspect-Level Sentiment Classification

  • Research
  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

Aspect-level sentiment classification seeks to ascertain the sentiment polarities of individual aspects within a sentence. Most existing research in this field focuses on individually assessing the importance of contexts on individual aspects, disregarding the negative impact of imbalanced relations between aspects due to their mutual influence. This paper presents a hybrid semantics and syntax-based graph convolutional network (SS-GCN) for aspect-level sentiment classification. This model addresses the imbalanced limitation by creating aspects-based balance relations between the strengths and weaknesses of different aspects through an auxiliary task. Furthermore, the multi-head self-attention mechanism utilizes position-enhanced encoding to identify the most relevant aspects of the current word. Extensive experiments demonstrate that SS-GCN outperforms other baselines in terms of classification performance. Compared to state-of-the-art methods, SS-GCN significantly improves 0.39–1.66% in accuracy and 0.43–1.92% in Macro-F1 on the SemEval 14-15 and MAMS datasets.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Algorithm 1
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data Availability

The datasets generated during and analyzed during the current study are available from the corresponding author upon reasonable request.

Notes

  1. https://paperswithcode.com/dataset/semeval-2014-task-4-sub-task-2

  2. https://paperswithcode.com/dataset/semeval-2014-task-4-sub-task-2

  3. https://alt.qcri.org/semeval2015/task12/index.php?id=data-and-tools

  4. https://paperswithcode.com/dataset/mams

References

  1. Birjali M, Kasri M, Beni-Hssane A. A comprehensive survey on sentiment analysis: approaches, challenges and trends. Knowl-Based Syst. 2021;226:107134.

  2. Gan C, Cao X, Zhu Q, Jain DK, García S. Enhancing microblog sentiment analysis through multi-level feature interaction fusion with social relationship guidance. Appl Intell. 2024;54(1):443–59.

    Google Scholar 

  3. Hao S, Zhang P, Liu S, Wang Y. Sentiment recognition and analysis method of official document text based on BERT-SVM model. Neural Comput & Applic. 2023;35(35):24621–32.

    Article  MATH  Google Scholar 

  4. Tang H, Ji D, Li C, Zhou Q. Dependency graph enhanced dual-transformer structure for aspect-based sentiment classification. In: Proceedings of the 58th annual meeting of the association for computational linguistics. 2020. pp 6578–6588.

  5. Verma S, Kumar A, Sharan A. IAN-BERT: combining post-trained BERT with interactive attention network for aspect-based sentiment analysis. SN Comput Sci. 2023;4(6):756.

    Article  MATH  Google Scholar 

  6. Gan C, Fu X, Feng Q, Zhu Q, Cao Y, Zhu Y. A multimodal fusion network with attention mechanisms for visual-textual sentiment analysis. Expert Syst Appl. 2024;242:122731.

  7. Lu Q, Sun X, Gao Z, Long Y, Feng J, Zhang H. Coordinated-joint translation fusion framework with sentiment-interactive graph convolutional networks for multimodal sentiment analysis. Inf Process Manag. 2024;61(1):103538.

  8. Aslan S. A deep learning-based sentiment analysis approach (MF-CNN-BILSTM) and topic modeling of tweets related to the Ukraine-Russia conflict. Appl Soft Comput. 2023;143:110404.

  9. Zhao Q, Mo Z, Fan M. POS-ATAEPE-BiLSTM: an aspect-based sentiment analysis algorithm considering part-of-speech embedding. Appl Intell. 2023;53(22):27440–58.

    Article  Google Scholar 

  10. Murugaiyan S, Uyyala SR. Aspect-based sentiment analysis of customer speech data using deep convolutional neural network and BiLSTM. Cognit Comput. 2023;15(3):914–31.

    Article  Google Scholar 

  11. Hussain A, Cambria E, Poria S, Hawalah AYA, Herrera F. Information fusion for affective computing and sentiment analysis. Inf Fusion. 2021;71:97–8.

    Article  Google Scholar 

  12. Anastasiou P, Tzafilkou K, Karapiperis D, Tjortjis C. Youtube sentiment analysis on healthcare product campaigns: combining lexicons and machine learning models. In: 2023 14th International conference on information, intelligence, systems & applications (IISA). 2023. pp 1–8.

  13. Wang Y, Huang G, Li M, Li Y, Zhang X, Li H. Automatically constructing a fine-grained sentiment lexicon for sentiment analysis. Cognit Comput. 2023;15(1):254–71.

    Article  MATH  Google Scholar 

  14. Li R, Chen H, Feng F, Ma Z, Wang X, Hovy E. Dual graph convolutional networks 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). 2021. pp 6319–6329.

  15. Wang Y, Zhao Y, Zhang Y, Derr T. Collaboration-aware graph convolutional networks for recommendation systems. 2022. CoRR arXiv:2207.06221.

  16. Dong Y, Liu Q, Du B, Zhang L. Weighted feature fusion of convolutional neural network and graph attention network for hyperspectral image classification. IEEE Trans Image Process. 2022;31:1559–72.

    Article  MATH  Google Scholar 

  17. Liu X, You X, Zhang X, Wu J, Lv P. Tensor graph convolutional networks for text classification. In: Proceedings of the AAAI conference on artificial intelligence. 2020. vol 34, pp 8409–8416

  18. Yang Z, Dong S. HAGERec: hierarchical attention graph convolutional network incorporating knowledge graph for explainable recommendation. Knowl-Based Syst. 2020;204:106194.

  19. Feng A, Gao Z, Song X, Ke K, Xu T, Zhang X. Modeling multi-targets sentiment classification via graph convolutional networks and auxiliary relation. CMC-Comput Mater Continua. 2020;64(2):909–23.

    Article  MATH  Google Scholar 

  20. Zeng J, Liu T, Jia W, Zhou J. Relation construction for aspect-level sentiment classification. Inf Sci. 2022;586:209–23.

    Article  MATH  Google Scholar 

  21. Fadel AS, Saleh ME, Salama R, Abulnaja OA. MTL-AraBERT: an enhanced multi-task learning model for Arabic aspect-based sentiment analysis. Computers. 2024;13(4):98.

    Article  Google Scholar 

  22. Rani S, Jain A. Aspect-based sentiment analysis of drug reviews using multi-task learning based dual BiLSTM model. Multimed Tools Appl. 2024;83(8):22473–501.

    Article  MATH  Google Scholar 

  23. 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, ACL 2020, Online. 2020. pp 3198–3210.

  24. Li Y, Lin Z, Lin Y, Yin J, Chang L. Learning sentiment-enhanced word representations by fusing external hybrid sentiment knowledge. Cognit Comput. 2023;15(6):1973–87.

    Article  Google Scholar 

  25. Wang K, Shen W, Yang Y, Quan X, Wang R. Relational graph attention network for aspect-based sentiment analysis. In: Proceedings of the 58th annual meeting of the association for computational linguistics, ACL 2020, Online. 2020. pp 3229–3238.

  26. Ping Z, Sang G, Liu Z, Zhang Y. Aspect category sentiment analysis based on prompt-based learning with attention mechanism. Neurocomputing. 2024;565:126994.

  27. Feng A, Cai J, Gao Z, Li X. Aspect-level sentiment classification with fused local and global context. J Big Data. 2023;10(1):176.

    Article  MATH  Google Scholar 

  28. Jain DK, Qamar S, Sangwan SR, Ding W, Kulkarni AJ. Ontology-based natural language processing for sentimental knowledge analysis using deep learning architectures. ACM Trans Asian Low-Resource Lang Inf Process. 2024;23(1):17–11717.

    Google Scholar 

  29. Liang S, Wei W, Mao X-L, Wang F, He Z. BiSyn-GAT+: bi-syntax aware graph attention network for aspect-based sentiment analysis. In: Findings of the association for computational linguistics: ACL 2022. 2022. pp 1835–1848.

  30. Song Y, Wang J, Liang Z, Liu Z, Jiang T. Utilizing BERT intermediate layers for aspect based sentiment analysis and natural language inference. 2020. CoRR arXiv:2002.04815.

  31. Wankhade M, Annavarapu CSR, Abraham A. MAPA BiLSTM-BERT: multi-aspects position aware attention for aspect level sentiment analysis. J Supercomput. 2023;79(10):11452–77.

    Article  Google Scholar 

  32. Chen D, Chen J, Yang L, Shang F. Mix-tower: light visual question answering framework based on exclusive self-attention mechanism. Neurocomputing. 2024;587:127686.

  33. Zhang W, Deng Y, Li X, Bing L, Lam W. Aspect-based sentiment analysis in question answering forums. In: Findings of the association for computational linguistics: EMNLP 2021. 2021. pp 4582–4591.

  34. Zhao P, Hou L, Wu O. Modeling sentiment dependencies with graph convolutional networks for aspect-level sentiment classification. Knowl-Based Syst. 2020;193:105443.

  35. 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.

  36. Wang X, Fan M, Kong M, Pei Z. Sentiment lexical strength enhanced self-supervised attention learning for sentiment analysis. Knowl-Based Syst. 2022;252:109335.

  37. Bhuvaneshwari P, Rao AN, Robinson YH, Thippeswamy MN. Sentiment analysis for user reviews using Bi-LSTM self-attention based CNN model. Multimed Tools Appl. 2022;81(9):12405–19.

  38. Pimpalkar A, Raj RJR. Mbilstmglove: Embedding glove knowledge into the corpus using multi-layer BiLSTM deep learning model for social media sentiment analysis. Expert Syst Appl. 2022;203:117581.

  39. Gan C, Zheng J, Zhu Q, Jain DK, Struc V. A graph neural network with context filtering and feature correction for conversational emotion recognition. Inf Sci. 2024;658:120017.

  40. Phan HT, Nguyen NT, Hwang D. Aspect-level sentiment analysis: a survey of graph convolutional network methods. Inf Fusion. 2023;91:149–72.

    Article  MATH  Google Scholar 

  41. Jiang B, Xu G, Liu P. Aspect-level sentiment classification via location enhanced aspect-merged graph convolutional networks. J Supercomput. 2023;79(9):9666–91.

    Article  Google Scholar 

  42. Jain DK, Zhao X, González-Almagro G, Gan C, Kotecha K. Multimodal pedestrian detection using metaheuristics with deep convolutional neural network in crowded scenes. Inf Fusion. 2023;95:401–14.

    Article  Google Scholar 

  43. Huang Y, Liu Q, Peng H, Wang J, Yang Q, Orellana-Martín D. Sentiment classification using bidirectional LSTM-SNP model and attention mechanism. Expert Syst Appl. 2023;221:119730.

  44. Liu J, Zhang Y. Attention modeling for targeted sentiment. In: Proceedings of the 15th conference of the european chapter of the association for computational linguistics. 2017. vol 2, pp 572–577.

  45. Ayetiran EF. Attention-based aspect sentiment classification using enhanced learning through cnn-bilstm networks. Knowl-Based Syst. 2022;252:109409.

  46. Basiri ME, Nemati S, Abdar M, Cambria E, Acharya UR. ABCDM: an attention-based bidirectional CNN-RNN deep model for sentiment analysis. Futur Gener Comput Syst. 2021;115:279–94.

    Article  Google Scholar 

  47. Fan F, Feng Y, Zhao D. Multi-grained attention network for aspect-level sentiment classification. In: Proceedings of the 2018 conference on empirical methods in natural language processing. 2018. pp 3433–3442.

  48. Huang B, Carley KM. Syntax-aware aspect level sentiment classification with graph attention networks. 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. 2019. pp 5468–5476.

  49. 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. pp 5679–5688.

  50. Liang B, Yin R, Gui L, Du J, Xu R: Jointly learning aspect-focused and inter-aspect relations with graph convolutional networks for aspect sentiment analysis. In: Proceedings of the 28th international conference on computational linguistics. 2020. pp 150–161.

  51. Zhu X, Zhu L, Guo J, Liang S, Dietze S. GL-GCN: global and local dependency guided graph convolutional networks for aspect-based sentiment classification. Expert Syst Appl. 2021;186:115712.

Download references

Funding

This work is partially supported by the Yibin Science and Technology Program (No. 2023SF004), the Sichuan Science and Technology Program (Nos. 2022YFG0378, 2023YFS0424, and 2023YFQ0044), and the Engineering Research Center for ICH Digitalization and Multi-source Information Fusion (Fujian Polytechnic Normal University), Fujian Province University (G3- KF2022).

Author information

Authors and Affiliations

  1. School of Computer and Software Engineering, Xihua University, Chengdu, 610039, China

    Chen Huang, Xianyong Li, Yajun Du & Dong Huang

  2. School of Information Science and Technology, Tibet University, Lhasa, 850000, China

    Zhicheng Dong

  3. Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian, 116024, China

    Deepak Kumar Jain

  4. Symbiosis Institute of Technology, Symbiosis International University, Pune, 412115, India

    Deepak Kumar Jain

  5. Centre of Al and Robotics, Edinburgh Napier University, Scotland, UK

    Amir Hussain

  6. Yibin Weite Ruian Technology Co., LTD, Yibin, 644600, China

    Xianyong Li

Authors
  1. Chen Huang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Xianyong Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Yajun Du
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Zhicheng Dong
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Dong Huang
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Deepak Kumar Jain
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Amir Hussain
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Chen Huang: methodology, software, data curation, writing—original draft. Xianyong Li: supervision, writing—review and editing, funding acquisition, formal analysis. Yajun Du: funding acquisition, investigation, validation. Zhicheng Dong: formal analysis, software. Dong Huang: formal analysis, data curation. Deepak Kumar Jain: formal analysis, supervision, writing—review and editing. Amir Hussain: formal analysis, supervision, writing—review and editing. All authors contributed to the manuscript revision and read and approved the submitted version.

Corresponding author

Correspondence to Deepak Kumar Jain.

Ethics declarations

Conflict of Interest

The authors declare no competing interests.

Ethical Approval and Informed Consent

All sources of funding are our research projects. There are no potential conflict of interest. Human participants and animals do not involve in this research. Informed consent for data used has been included in this study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, C., Li, X., Du, Y. et al. A Hybrid Semantics and Syntax-Based Graph Convolutional Network for Aspect-Level Sentiment Classification. Cogn Comput 17, 16 (2025). https://doi.org/10.1007/s12559-024-10367-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12559-024-10367-0

Keywords