Volume 20, Issue 4 (3-2024)
JSDP 2024, 20(4): 121-128 |
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karimi S, Jafarinejad F. Aspect-Based Sentiment Analysis using the Attentional Encoder Network. JSDP 2024; 20 (4) : 8
URL: http://jsdp.rcisp.ac.ir/article-1-1321-en.html
URL: http://jsdp.rcisp.ac.ir/article-1-1321-en.html
Shahrood University of Technology
Abstract: (412 Views)
Natural language processing is growing significantly and has gained much attention with the advent of the World Wide Web and search engines, and researchers have witnessed an explosion of information in different languages. Sentiment analysis is one of the most active fields of study in natural language processing that focuses on text classification and is used to identify, extract and analyze subjective information from text sources. Aspect-based sentiment analysis is a text analysis technique that classifies comments by aspect and identifies the sentiment associated with each aspect. This analysis can be used to automatically analyze the feedback of customers' comments to different parts of goods or services and help employers to focus on points that need quality improvement. In this paper, we will introduce a new architecture based on deep learning for aspect-based sentiment analysis. This architecture will use an attention-encoder network-based model with multiple multi-head attention and a pointwise convolutional transform (which is a parallelizable and interactive alternative to LSTM and is applied to compute hidden states of input embeddings). Testing this architecture on three different datasets, including restaurants and laptops, SemEval 2014 Task 4 and ACL 14 Twitter dataset, in all three datasets, the polarity of emotions is positive, neutral and negative, which is compared with modern methods of sentiment analysis. Based on the aspect, it will show the high accuracy of this method. For example, the aspect-based sentiment analysis on the Laptop dataset has shown 79.15% accuracy, which has increased the accuracy by 4.24% compared to modern methods.
Article number: 8
Keywords: Aspect-Based Sentiment Analysis (ABSA), Deep Learning, Attentional Encoder Network, Multi-Head Attention (MHA)
Type of Study: Research |
Subject:
Paper
Received: 2022/06/28 | Accepted: 2023/12/9 | Published: 2024/04/25 | ePublished: 2024/04/25
Received: 2022/06/28 | Accepted: 2023/12/9 | Published: 2024/04/25 | ePublished: 2024/04/25
References
1. v[1] Z. Rajabi, M. valavi, and M. Hourali, "Sentiment analysis methods in Persian text: A survey," Signal Data Process., vol. 19, no. 2, pp. 107-132, 2022, doi: 10.52547/jsdp.19.2.107. [DOI:10.52547/jsdp.19.2.107]
2. [2]"https://daneshyari.com/isi/articles/sentiment_anal."
3. [3] S. Behdenna, F. Barigou, and G. Belalem, "EAI Endorsed Transactions Document Level Sentiment Analysis : A survey," vol. 4, no. 1, pp. 1-8, 2017. [DOI:10.4108/eai.14-3-2018.154339]
4. [4] V. S. Jagtap and K. Pawar, "Analysis of different approaches to Sentence-Level Sentiment Classification," Int. J. Sci. Eng. Technol., vol. 2, no. 3, pp. 164-170, 2013, [Online]. Available: http://ijset.com/ijset/ publication/v2s3/paper11.pdf
5. [5] H. Wan, Y. Yang, J. Du, Y. Liu, K. Qi, and J. Z. Pan, "Target-aspect-sentiment joint detection for aspect-based sentiment analysis," AAAI 2020 - 34th AAAI Conf. Artif. Intell., pp. 9122-9129, 2020, doi: 10.1609/aaai.v34i05.6447. [DOI:10.1609/aaai.v34i05.6447]
6. [6] Y. Kim, "Convolutional neural networks for sentence classification," EMNLP 2014 - 2014 Conf. Empir. Methods Nat. Lang. Process. Proc. Conf., pp. 1746-1751, 2014, doi: 10.3115/v1/d14-1181. [DOI:10.3115/v1/D14-1181]
7. [7] A. K. Sharma, S. Chaurasia, and D. K. Srivastava, "Sentimental Short Sentences Classification by Using CNN Deep Learning Model with Fine Tuned Word2Vec," Procedia Comput. Sci., vol. 167, no. 2019, pp. 1139-1147, 2020, doi: 10.1016/j.procs.2020.03.416. [DOI:10.1016/j.procs.2020.03.416]
8. [8] S. Ramaswamy and N. DeClerck, "Customer perception analysis using deep learning and NLP," Procedia Comput. Sci., vol. 140, pp. 170-178, 2018, doi: 10.1016/j.procs.2018-.10.326. [DOI:10.1016/j.procs.2018.10.326]
9. [9] H. Sadr, M. mohsen Pedram, and M. Teshnehlab, "Efficient Method Based on Combination of Deep Learning Models for Sentiment Analysis of Text," Signal Data Process., vol. 19, no. 1, pp. 19-38, 2022, doi: 10.52547/jsdp.19.1.19. [DOI:10.52547/jsdp.19.1.19]
10. [10] Y. Wang, M. Huang, L. Zhao, and X. Zhu, "Attention-based LSTM for aspect-level sentiment classification," EMNLP 2016 - Conf. Empir. Methods Nat. Lang. Process. Proc., pp. 606-615, 2016, doi: 10.18653/v1/d16-1058. [DOI:10.18653/v1/D16-1058]
11. [11] T. Chen, R. Xu, Y. He, and X. Wang, "Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN," Expert Syst. Appl., vol. 72, pp. 221-230, 2017, doi: 10.1016/j.eswa.2016.10.065. [DOI:10.1016/j.eswa.2016.10.065]
12. [12] D. Tang, B. Qin, X. Feng, and T. Liu, "Effective LSTMs for target-dependent sentiment classification," COLING 2016 - 26th Int. Conf. Comput. Linguist. Proc. COLING 2016 Tech. Pap., pp. 3298-3307, 2016.
13. [13] Y. Song, J. Wang, T. Jiang, Z. Liu, and Y. Rao, "Targeted Sentiment Classification with Attentional Encoder Network," Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 11730 LNCS, pp. 93-103, 2019, doi: 10.1007/978-3-030-30490-4_9. [DOI:10.1007/978-3-030-30490-4_9]
14. [14] M. Pontiki, D. Galanis, H. Papageorgiou, S. Manandhar, and I. Androutsopoulos, "SemEval-2015 Task 12: Aspect Based Sentiment Analysis," SemEval 2015 - 9th Int. Work. Semant. Eval. co-located with 2015 Conf. North Am. Chapter Assoc. Comput. Linguist. Hum. Lang. Technol. NAACL-HLT 2015 - Proc., pp. 486-495, 2015, doi: 10.18653/v1/s15-2082. [DOI:10.18653/v1/S15-2082]
15. [15] L. Dong, F. Wei, C. Tan, D. Tang, M. Zhou, and K. Xu, "Adaptive Recursive Neural Network for target-dependent Twitter sentiment classification," 52nd Annu. Meet. Assoc. Comput. Linguist. ACL 2014 - Proc. Conf., vol. 2, pp. 49-54, 2014, doi: 10.3115/v1/p14-2009. [DOI:10.3115/v1/P14-2009] []
16. [16] T. S. Ataei, K. Darvishi, S. Javdan, B. Minaei-Bidgoli, and S. Eetemadi, "Pars-ABSA: an Aspect-based Sentiment Analysis dataset for Persian," pp. 1-6, 2019.
17. [17] G. Pang, K. Lu, X. Zhu, J. He, Z. Mo, and Z. Peng, "Aspect-Level Sentiment Analysis Approach via BERT and Aspect Feature Location Model," vol. 2021, 2021. [DOI:10.1155/2021/5534615]
18. [18] Z. Sun, L. Bing, W. Yang, and P. Chen, "Recurrent Attention Network on Memory for Aspect Sentiment Analysis," pp. 452-461, 2017.
19. [19] R. Wang, "Interactive Attention Encoder Network with Local Context Features for Aspect-Level Sentiment Analysis," no. Iccc, pp. 571-576, 2020, doi: 10.1109/ICCC49849. 2020.9238924. [DOI:10.1109/ICCC49849.2020.9238924]
20. [20] F. Fan, Y. Feng, and D. Zhao, "Multi-grained Attention Network for Aspect-Level Sentiment Classification," pp. 3433-3442, 2018. [DOI:10.18653/v1/D18-1380]
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