A Deep Learning-fuzzy Based Hybrid Ensemble Approach for Aspect Level Sentiment Classification

Tanu Sharma, Kamaldeep Kaur

Abstract


Aspect level sentiment classification (ALSC) has gained high importance in the era of e-commerce based economy.  It allows manufacturers to improve the designs of their products based on users’ feedback. However, only a few datasets of limited domains are available for ALSC task. To push forward the research in automated ALSC, this study contributes cars dataset of the automobile domain. In this study, a novel fuzzy ensemble technique is also proposed based upon the mathematical analysis of confidence scores of base deep neural networks. The proposed approach allows to correct the misclassifications of base deep learners through a reward and penalization strategy. The experimental results on five benchmark datasets show that the proposed approach outperforms the constituent base deep neural networks and several other important baselines. The proposed Fuzzy ensemble also performed at par with the most recent Graph Convolution Neural Networks on basis of Friedman and Nemenyi Tests.


Full Text:

PDF

References


K. Schouten and F. Frasincar, "Survey on Aspect-Level Sentiment Analysis," IEEE Transactions on Knowledge & Data Engineering , vol. 28, no. 3, pp. 813-830, March 2016.

T. Sharma and K. Kaur, "Benchmarking Deep Learning Methods for Aspect Level Sentiment Classification," Applied Sciences, vol. 11(22):10542, November 2021.

M. Pontiki, D. Galanis, J. Pavlopoulos, H. Papageorgiou, I. Androutsopoulos and S. Manandhar, "SemEval-2014 Task 4: Aspect Based Sentiment Analysis," in 8th International Workshop on Semantic Evaluation (SemEval 2014), Dublin, Ireland, 2014.

M. Pontiki, D. Galanis, H. Papageorgiou, S. Manandhar and I. Androutsopoulos, "Semeval-2015 task 12: Aspect based sentiment analysis.," in 9th International Workshop on Semantic Evaluation (SemEval 2015), 2015.

M. Pontiki, D. Galanis, H. Papageorgiou, I. Androutsopoulos, S. Manandhar and A. S. Mohammad, "SemEval-2016 task 5: Aspect based sentiment analysis.," in 10th international workshop on semantic evaluation (SemEval-2016), 2016.

Q. Jiang, L. Chen, R. Xu, X. Ao and M. Yang, "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, 2019.

L. Dong, F. Wei, C. Tan, D. Tang, M. Zhou and K. Xu, "Adaptive Recursive Neural Networkfor target-dependent twitter sentiment classification," in Proceedings of the 52nd annual meeting of the association for computational linguistics, Baltimore, Maryland, USA, 2014.

W. Etaiwi, D. Suleiman and A. Awajan, "Deep Learning Based Techniques for Sentiment Analysis: A Survey," Informatica, vol. 45, no. 7, pp. 89-95, 2021.

https://doi.org/10.31449/inf.v45i7.3674

S. Al-Otaibi and A. Al-Rasheed, "A Review and Comparative Analysis of Sentiment Analysis Techniques," Informatica, vol. 46, no. 6, pp. 33-44, 2022.

https://doi.org/10.31449/inf.v46i6.3991

J. Zhou, J. X. Huang, Q. Chen, Q. V. Hu, T. Wang and L. He, "Deep Learning for Aspect-Level Sentiment Classification: Survey, Vision and Challenges.," IEEE Access, vol. 7, 2019.

D. Tang, B. Qin, X. Feng and T. Liu, "Effective LSTMs for Target-Dependent Sentiment Classification," arXiv preprint arXiv:1512.01100, 2016.

Y. Wang, M. Huang, L. Zhao and X. Zhu, "Attention-based LSTM for Aspect-level Sentiment Classification," in Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 2016.

P. Chen, L. Bing, Z. Sun and W. Yang, "Recurrent Attention Network on Memory for Aspect Sentiment Analysis," in Conference on Empirical Methods in Natural Language Processing, 2017.

D. Tang, B. Qin and T. Liu, "Aspect Level Sentiment Classification with Deep Memory Network," in Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 2016.

W. Xue and T. Li, "Aspect Based Sentiment Analysis with Gated Convolutional Networks," in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 2018.

X. Li, L. Bing, W. Lam and B. Shi, "Transformation networks for target-oriented sentiment classification," in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, 2018.

B. Huang, Y. Ou and K. M. Carley, "Aspect Level Sentiment Classification with Attention-over-Attention Neural Networks," in International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation,SBP-BRiMS 2018, 2018.

D. Ma, S. Li, X. Zhang and H. Wang, "Interactive Attention Networks for Aspect-Level Sentiment Classification," arXiv preprint arXiv:1709.00893 , 2017.

F. Fan, Y. Feng and D. Zhao, "Multi-grained attention network for aspect-level sentiment classification," in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, 2018.

C. Zhang, Q. Li and D. Song, "Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks," in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019.

B. Huang and K. M. Carley, "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.

R. Li, H. Chen, F. Feng, Z. Ma, X. Wang and E. Hovy, "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), Online, 2021.

Z. Zhang, Z. Zhou and Y. Wang, "SSEGCN: Syntactic and Semantic Enhanced Graph Convolutional Network for Aspect-based Sentiment Analysis," in Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Seattle, United States, 2022.

H. Wu, C. Huang and S. Deng, "Improving aspect-based sentiment analysis with Knowledge-aware Dependency Graph Network," Information Fusion, vol. 92, pp. 289-299, 2022.

B. Liang, H. Su, L. Gui, E. Cambria and R. Xu, "Aspect-based sentiment analysis via affective knowledge enhanced graph convolutional networks," Knowledge-Based Systems, vol. 235, p. 107643, 2022.

X. Bai, P. Liu and Y. Zhang, "Investigating Typed Syntactic Dependencies for Targeted Sentiment Classification Using Graph Attention Neural Network," IEEE/ACM Transactions on Audio, Speech, and Language Processing, pp. 503-514, 2021.

X. Zhu, L. Zhu, J. Guo, S. Liang and S. Dietze, "GL-GCN: Global and Local Dependency Guided Graph Convolutional Networks for aspect-based sentiment classification," Expert Systems With Applications, vol. 186, 2021.

T. Sharma and K. Kaur, "An Ensemble approach for Aspect level sentiment classification using deep learning methods," in 3rd International Conference on Data Analytics & Management (ICDAM-2022), 2022.

T. Sharma and K. Kaur, "An Equilibrium Optimizer based Ensemble for Aspect level Sentiment Classification," in presented at International Conference on Advances and Applications of Artificial Intelligence and Machine Learning(ICAAAIML), 2022.

A. Mohammadi and A. Shaverizade, "Ensemble Deep Learning for Aspect-based Sentiment Analysis," International Journal of Nonlinear Analysis and Applications, vol. 12, no. Special Issue, Winter and Spring 2021, pp. 29-38, 2021.

T. Sharma and K. Kaur, "Aspect sentiment classification using syntactic neighbour based attention network," Journal of King Saud University - Computer and Information Sciences, vol. 35, no. 2, pp. 612-625, 2023.

https://doi.org/10.1016/j.jksuci.2023.01.005

K. Ganesan and C. Zhai, "Opinion-based entity ranking," Information Retrieval, vol. 15, no. 2, pp. 116-150, 2012.

H. Rinne, The Weibull Distribution A Handbook, 1st ed., Chapman & Hall, 2020.




DOI: https://doi.org/10.31449/inf.v47i6.4607

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.