Hybrid Deep Neural Network Model for Aspect and Opinion Extraction with Multi-Head Attention-Driven Sentiment Analysis
Abstract
Finding and extracting significant features from review sentences is known as aspect triplet extraction, and it provides succinct information on the elements that users have addressed. This method makes sentiment analysis and opinion mining easier, which helps to provide an adequate understanding of user opinions in reviews. This research presents a novel approach to achieve Aspect-Sentiment Triplet-Extraction (ASTE) using a deep neural network and transformer-based multi-head attention model. The proposed hybrid model adopts a pipeline methodology, concurrently extracting opinions and aspects while performing sentiment classification. The study addresses the intricate challenge of identifying triplets that capture nuanced relationships between terms and sentences, employing a deep neural network for joint extraction of aspects and opinions using a sequential tagging method. Sentiment classification is seamlessly integrated into the pipeline, treating sentiment recognition as a classification task, and aspect and opinion extraction as text-extraction challenges. Evaluations was out experimentally on the SemEval 2016 restaurant dataset demonstrate the effectiveness of the model, despite issues with unequal distribution of data.
Keywords
Full Text:
PDFReferences
Chen, S. et al. (2021) “Bidirectional machine reading comprehension for aspect sentiment triplet extraction,” Proceedings of the AAAI Conference on Artificial Intelligence, 35(14), pp. 12666–12674. Available at:https://doi.org/10.1609/aaai.v35i14.17500.
Dai, H. and Song, Y. (2019) “Neural aspect and opinion term extraction with mined rules as weak supervision,” Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics [Preprint]. Available at:https://doi.org/10.18653/v1/p19-1520.
Kamath, U., Graham, K.L. and Emara, W. (2022) “Bidirectional encoder representations from Transformers (Bert),” Transformers for Machine Learning, pp. 43–70. Available at:https://doi.org/10.1201/9781003170082-3.
Huang, Lianzhe& Wang, Peiyi& Li, Sujian& Liu, Tianyu& Zhang, Xiaodong & Cheng, Zhicong& Yin, Dawei & Wang, Houfeng. (2021). First Target and Opinion then Polarity: Enhancing Target-opinion Correlation for Aspect Sentiment Triplet Extraction.
Li, X. et al. (2019) “A unified model for opinion target extraction and target sentiment prediction,” Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), pp. 6714–6721. Available at:https://doi.org/10.1609/aaai.v33i01.33016714.
Li, X. et al. (2019) “Entity-relation extraction as multi-turn question answering,” Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics [Preprint]. Available at:https://doi.org/10.18653/v1/p19-1129.
Peng, H. et al. (2020) “Knowing what, how and why: A near complete solution for aspect-based sentiment analysis,” Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), pp. 8600–8607. Available at:https://doi.org/10.1609/aaai.v34i05.6383.
Poria, S. et al. (2020) “Beneath the tip of the iceberg: Current challenges and New Directions in sentiment analysis research,” IEEE Transactions on Affective Computing, pp. 1–1. Available at:https://doi.org/10.1109/taffc.2020.3038167.
Wang, W. et al. (2017) “Coupled multi-layer attentions for co-extraction of aspect and opinion terms,” Proceedings of the AAAI Conference on Artificial Intelligence, 31(1). Available at:https://doi.org/10.1609/aaai.v31i1.10974.
Wu, Z. et al. (2020) “Grid tagging scheme for aspect-oriented fine-grained opinion extraction,” Findings of the Association for Computational Linguistics: EMNLP 2020 [Preprint]. Available at:https://doi.org/10.18653/v1/2020.findings-emnlp.234.
Xu, L. et al. (2020) “Position-aware tagging for aspect sentiment triplet extraction,” Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) [Preprint]. Available at:https://doi.org/10.18653/v1/2020.emnlp-main.183.
Zhang, C. et al. (2020) “A multi-task learning framework for opinion triplet extraction,” Findings of the Association for Computational Linguistics: EMNLP 2020 [Preprint]. Available at:https://doi.org/10.18653/v1/2020.findings-emnlp.72.
Li, X. et al. (2018) “Aspect term extraction with history attention and selective transformation,” Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence [Preprint]. Available at:https://doi.org/10.24963/ijcai.2018/583.
Li, X. and Lam, W. (2017) “Deep multi-task learning for aspect term extraction with memory interaction,” Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing [Preprint]. Available at:https://doi.org/10.18653/v1/d17-1310.
Luo, H. et al. (2019) “Doer: Dual cross-shared RNN for aspect term-polarity co-extraction,” Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics [Preprint]. Available at:https://doi.org/10.18653/v1/p19-1056..
Yichun Yin, et al. (2016), “Unsupervised Word and Dependency Path Embeddings for Aspect Term Extraction,” Available at:https://doi.org/10.48550/arXiv.1605.07843.
Yu Bai Jian, S. et al. (2021) “Aspect sentiment triplet extraction using reinforcement learning,” Proceedings of the 30th ACM International Conference on Information & Knowledge Management [Preprint]. Available at:https://doi.org/10.1145/3459637.3482058.
Pontiki, M. et al. (2014) “Semeval-2014 task 4: Aspect based sentiment analysis,” Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014) [Preprint]. Available at:https://doi.org/10.3115/v1/s14-2004.
Pontiki, M. et al. (2016) “Semeval-2016 task 5: Aspect based sentiment analysis,” Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016) [Preprint]. Available at:https://doi.org/10.18653/v1/s16-1002.
Hercig, T. (2018) “UWB at Semeval-2018 task 3: Irony detection in English tweets,” Proceedings of the 12th International Workshop on Semantic Evaluation [Preprint]. Available at:https://doi.org/10.18653/v1/s18-1084.
S. Chaitusaney and A. Yokoyama, “An Appropriate Distributed Generation Sizing Considering Recloser-Fuse Coordination,” in 2005 IEEE/PES Transmission & Distribution Conference & Exposition: Asia and Pacific, pp. 1–6, doi: 10.1109/TDC.2005.1546838.
Haiyun Peng, Lu Xu, Lidong Bing, Fei Huang,Wei Lu, and Luo Si. 2020. Knowing what, howand why: A near complete solution for aspect-based sentiment analysis. In Proceedings of the AAAI Conference on Artificial Intelligence. https://aaai.org/ ojs/index.php/AAAI/article/view/6383.
Lu Xu, Hao Li,Wei Lu, and Lidong Bing. 2020. Position-Aware Tagging for Aspect Sentiment Triplet Extraction. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing.https://doi.org/10.18653/v1/2020.emnlpmain. 183.
A. Oudalov et al., “Novel Protection Systems for Microgrids,” 2009. [Online]. Available: http://www.microgrids.eu/documents/688.pds
Kevin Scaria, Himanshu Gupta et al., “InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis,” 2024. Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers), pages 720–736, Mexico City, Mexico. Association for Computational Linguistics. [Online]. Available: 10.18653/v1/2024.naacl-short.63
DOI: http://doi.org/10.11591/ijict.v15i2.pp769-777
Refbacks
- There are currently no refbacks.
Copyright (c) 2026 Abhinandan Shirahatti, Vijay Rajpurohit, Ramesh Medar
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
The International Journal of Informatics and Communication Technology (IJ-ICT)
p-ISSN 2252-8776, e-ISSN 2722-2616
This journal is published by the Intelektual Pustaka Media Utama (IPMU).