Explicit aspect extraction techniques: Review
DOI:
https://doi.org/10.29304/jqcm.2022.14.4.1116Keywords:
sentiment analysis, Natural language processing, Aspect extraction, Sentiment classification, Explicit aspectsAbstract
Sentiment analysis is gathering opinion keywords, such as aspects, opinions, or features- and figuring out their semantic perspective relations. More specifically, aspect-based sentiment analysis – (ABSA) is a subfield of natural language that focuses on phrases associated with aspects and detects the sentiment that belongs to each one. Aspect extraction and sentiment analysis are the two fundamental functions in ABSA, and the different classes of aspects are explicit and implicit. To shed some light on this problem, we discuss aspect extraction tasks and classify explicit aspect extraction strategies into two categories: supervised, unsupervised and semi-supervised. This article explores prior research and their approaches by reviewing works from 2016 to 2021 and comparing numerous elements comprehensively, including classifying systems, classifier methods, datasets, and performance.
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References
Yu, H. and V. Hatzivassiloglou. Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences. in Proceedings of the 2003 conference on Empirical methods in natural language processing. 2003.
2. Das, A. and W.E. Zhang. ABSA-Bench: Towards the Unified Evaluation of Aspect-based Sentiment Analysis Research. in Proceedings of the The 18th Annual Workshop of the Australasian Language Technology Association. 2020.
3. Khudeyer, R.S., M. Alabbas, and M. Radif, Multi-font arabic isolated character recognition using combining machine learning classifiers. journal of southwest jiaotong university, 2020. 55(1).
4. Hamoud, A.K., et al. Improving service quality using consumers’ complaints data mart which effect on financial customer satisfaction. in Journal of Physics: Conference Series. 2020. IOP Publishing.
5. Poria, S., E. Cambria, and A. Gelbukh, Aspect extraction for opinion mining with a deep convolutional neural network. Knowledge-Based Systems, 2016. 108: p. 42-49.
6. Gojali, S. and M.L. Khodra. Aspect based sentiment analysis for review rating prediction. in 2016 International Conference On Advanced Informatics: Concepts, Theory And Application (ICAICTA). 2016. IEEE.
7. Xu, L., et al., Aspect based sentiment analysis for online reviews, in Advances in computer science and ubiquitous computing. 2017, Springer. p. 475-480.
8. Nasim, Z. and S. Haider, ABSA toolkit: An open source tool for aspect based sentiment analysis. International Journal on Artificial Intelligence Tools, 2017. 26(06): p. 1750023.
9. Liu, N. and B. Shen, Aspect-based sentiment analysis with gated alternate neural network. Knowledge-Based Systems, 2020. 188: p. 105010.
10. Jihan, N., Y. Senarath, and S. Ranathunga. Aspect extraction from customer reviews using convolutional neural networks. in 2018 18th International Conference on Advances in ICT for Emerging Regions (ICTer). 2018. IEEE.
11. Zhao, N., et al., Combination of convolutional neural network and gated recurrent unit for aspect-based sentiment analysis. IEEE Access, 2021. 9: p. 15561-15569.
12. Ahmed, S., et al., Aspect based sentiment analysis framework using data from social media network. IJCSNS Int. J. Comput. Sci. Netw. Secur, 2017. 17: p. 100-105.
13. Mohammad, A.-S., et al. An enhanced framework for aspect-based sentiment analysis of Hotels' reviews: Arabic reviews case study. in 2016 11th International conference for internet technology and secured transactions (ICITST). 2016. IEEE.
14. Pannala, N.U., et al. Supervised learning based approach to aspect based sentiment analysis. in 2016 IEEE international conference on computer and information technology (CIT). 2016. IEEE.
15. Piryani, R., et al., A linguistic rule-based approach for aspect-level sentiment analysis of movie reviews, in Advances in computer and computational sciences. 2017, Springer. p. 201-209.
16. Wu, C., et al., A hybrid unsupervised method for aspect term and opinion target extraction. Knowledge-Based Systems, 2018. 148: p. 66-73.
17. Firmanto, A. and R. Sarno, Aspect-based sentiment analysis using grammatical rules, word similarity and sentiCircle. International Journal of Intelligent Engineering and Systems, 2019. 12(5): p. 190-201.
18. Rana, T.A. and Y.-N. Cheah. Improving aspect extraction using aspect frequency and semantic similarity-based approach for aspect-based sentiment analysis. in International conference on computing and information technology. 2017. Springer.
19. Rana, T.A. and Y.-N. Cheah, Sequential patterns rule-based approach for opinion target extraction from customer reviews. Journal of Information Science, 2019. 45(5): p. 643-655.
20. Jiménez-Zafra, S.M., et al., Combining resources to improve unsupervised sentiment analysis at aspect-level. Journal of Information Science, 2016. 42(2): p. 213-229.
21. Li, S., L. Zhou, and Y. Li, Improving aspect extraction by augmenting a frequency-based method with web-based similarity measures. Information Processing & Management, 2015. 51(1): p. 58-67.
22. Amplayo, R.K. and S.-w. Hwang. Aspect sentiment model for micro reviews. in 2017 IEEE International Conference on Data Mining (ICDM). 2017. IEEE.
23. Ding, Y., C. Yu, and J. Jiang. A neural network model for semi-supervised review aspect identification. in Pacific-Asia Conference on Knowledge Discovery and Data Mining. 2017. Springer.
24. Matsuno, I.P., et al., Aspect-based sentiment analysis using semi-supervised learning in bipartite heterogeneous networks. Journal of Information and Data Management, 2016. 7(2): p. 141-141.
25. Wang, T., et al., Product aspect extraction supervised with online domain knowledge. Knowledge-Based Systems, 2014. 71: p. 86-100.
26. Berry, M.W., A. Mohamed, and B.W. Yap, Supervised and unsupervised learning for data science. 2019: Springer.
2. Das, A. and W.E. Zhang. ABSA-Bench: Towards the Unified Evaluation of Aspect-based Sentiment Analysis Research. in Proceedings of the The 18th Annual Workshop of the Australasian Language Technology Association. 2020.
3. Khudeyer, R.S., M. Alabbas, and M. Radif, Multi-font arabic isolated character recognition using combining machine learning classifiers. journal of southwest jiaotong university, 2020. 55(1).
4. Hamoud, A.K., et al. Improving service quality using consumers’ complaints data mart which effect on financial customer satisfaction. in Journal of Physics: Conference Series. 2020. IOP Publishing.
5. Poria, S., E. Cambria, and A. Gelbukh, Aspect extraction for opinion mining with a deep convolutional neural network. Knowledge-Based Systems, 2016. 108: p. 42-49.
6. Gojali, S. and M.L. Khodra. Aspect based sentiment analysis for review rating prediction. in 2016 International Conference On Advanced Informatics: Concepts, Theory And Application (ICAICTA). 2016. IEEE.
7. Xu, L., et al., Aspect based sentiment analysis for online reviews, in Advances in computer science and ubiquitous computing. 2017, Springer. p. 475-480.
8. Nasim, Z. and S. Haider, ABSA toolkit: An open source tool for aspect based sentiment analysis. International Journal on Artificial Intelligence Tools, 2017. 26(06): p. 1750023.
9. Liu, N. and B. Shen, Aspect-based sentiment analysis with gated alternate neural network. Knowledge-Based Systems, 2020. 188: p. 105010.
10. Jihan, N., Y. Senarath, and S. Ranathunga. Aspect extraction from customer reviews using convolutional neural networks. in 2018 18th International Conference on Advances in ICT for Emerging Regions (ICTer). 2018. IEEE.
11. Zhao, N., et al., Combination of convolutional neural network and gated recurrent unit for aspect-based sentiment analysis. IEEE Access, 2021. 9: p. 15561-15569.
12. Ahmed, S., et al., Aspect based sentiment analysis framework using data from social media network. IJCSNS Int. J. Comput. Sci. Netw. Secur, 2017. 17: p. 100-105.
13. Mohammad, A.-S., et al. An enhanced framework for aspect-based sentiment analysis of Hotels' reviews: Arabic reviews case study. in 2016 11th International conference for internet technology and secured transactions (ICITST). 2016. IEEE.
14. Pannala, N.U., et al. Supervised learning based approach to aspect based sentiment analysis. in 2016 IEEE international conference on computer and information technology (CIT). 2016. IEEE.
15. Piryani, R., et al., A linguistic rule-based approach for aspect-level sentiment analysis of movie reviews, in Advances in computer and computational sciences. 2017, Springer. p. 201-209.
16. Wu, C., et al., A hybrid unsupervised method for aspect term and opinion target extraction. Knowledge-Based Systems, 2018. 148: p. 66-73.
17. Firmanto, A. and R. Sarno, Aspect-based sentiment analysis using grammatical rules, word similarity and sentiCircle. International Journal of Intelligent Engineering and Systems, 2019. 12(5): p. 190-201.
18. Rana, T.A. and Y.-N. Cheah. Improving aspect extraction using aspect frequency and semantic similarity-based approach for aspect-based sentiment analysis. in International conference on computing and information technology. 2017. Springer.
19. Rana, T.A. and Y.-N. Cheah, Sequential patterns rule-based approach for opinion target extraction from customer reviews. Journal of Information Science, 2019. 45(5): p. 643-655.
20. Jiménez-Zafra, S.M., et al., Combining resources to improve unsupervised sentiment analysis at aspect-level. Journal of Information Science, 2016. 42(2): p. 213-229.
21. Li, S., L. Zhou, and Y. Li, Improving aspect extraction by augmenting a frequency-based method with web-based similarity measures. Information Processing & Management, 2015. 51(1): p. 58-67.
22. Amplayo, R.K. and S.-w. Hwang. Aspect sentiment model for micro reviews. in 2017 IEEE International Conference on Data Mining (ICDM). 2017. IEEE.
23. Ding, Y., C. Yu, and J. Jiang. A neural network model for semi-supervised review aspect identification. in Pacific-Asia Conference on Knowledge Discovery and Data Mining. 2017. Springer.
24. Matsuno, I.P., et al., Aspect-based sentiment analysis using semi-supervised learning in bipartite heterogeneous networks. Journal of Information and Data Management, 2016. 7(2): p. 141-141.
25. Wang, T., et al., Product aspect extraction supervised with online domain knowledge. Knowledge-Based Systems, 2014. 71: p. 86-100.
26. Berry, M.W., A. Mohamed, and B.W. Yap, Supervised and unsupervised learning for data science. 2019: Springer.
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Published
2022-12-31
How to Cite
Akram, A., & Sabir, A. S. (2022). Explicit aspect extraction techniques: Review. Journal of Al-Qadisiyah for Computer Science and Mathematics, 14(4), Comp Page 90–98. https://doi.org/10.29304/jqcm.2022.14.4.1116
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Section
Computer Articles
