A hybrid model for aspect-based sentiment analysis on customer feedback: research on the mobile commerce sector in Vietnam

(1) * Thanh Trung Ho Mail (University of Economics and Law, Ho Chi Minh City, Vietnam; Viet Nam National University, Ho Chi Minh City,, Viet Nam)
(2) Hien Minh Bui Mail (UEH College of Technology and Design (UEH-CTD), University of Economics Ho Chi Minh City (UEH), Vietnam, Viet Nam)
(3) Phung Kim Thai Mail (UEH College of Technology and Design (UEH-CTD), University of Economics Ho Chi Minh City (UEH), Vietnam, Viet Nam)
*corresponding author

Abstract


Feedback and comments on mobile commerce applications are extremely useful and valuable information sources that reflect the quality of products or services to determine whether data is positive or negative and help businesses monitor brand and product sentiment in customers’ feedback and understand customers’ needs. However, the increasing number of comments makes it increasingly difficult to understand customers using manual methods. To solve this problem, this study builds a hybrid research model based on aspect mining and comment classification for aspect-based sentiment analysis (ABSA) to deeply comprehend the customer and their experiences. Based on previous classification results, we first construct a dictionary of positive and negative words in the e-commerce field. Then, the POS tagging technique is applied for word classification in Vietnamese to extract aspects of model commerce related to positive or negative words. The model is implemented with machine and deep learning methods on a corpus comprising more than 1,000,000 customer opinions collected from Vietnam's four largest mobile commerce applications. Experimental results show that the Bi-LSTM method has the highest accuracy with 92.01%; it is selected for the proposed model to analyze the viewpoint of words on real data. The findings are that the proposed hybrid model can be applied to monitor online customer experience in real time, enable administrators to make timely and accurate decisions, and improve the quality of products and services to take a competitive advantage.

Keywords


customer feedback; sentiment analysis; mobile commerce; machine and deep learning; POS tagging; aspect extraction.

   

DOI

https://doi.org/10.26555/ijain.v9i2.976
      

Article metrics

Abstract views : 641 | PDF views : 203

   

Cite

   

Full Text

Download

References


[1] “The Map of E-commerce in Vietnam,” iprice, 2019. Accessed Apr. 19. 2021. [Online]. Available at: https://iprice.vn/insights/mapofecommerce/en/.

[2] B. Liu, M. Hu, and J. Cheng, “Opinion observer,” in Proceedings of the 14th international conference on World Wide Web - WWW ’05, 2005, p. 342, doi: 10.1145/1060745.1060797.

[3] “Vietnamese language,” Britannica. [Online]. Available at: https://www.britannica.com/topic/Vietnamese-language.

[4] Y. Nurdiansyah, S. Bukhori, and R. Hidayat, “Sentiment analysis system for movie review in Bahasa Indonesia using naive bayes classifier method,” J. Phys. Conf. Ser., vol. 1008, no. 1, p. 012011, Apr. 2018, doi: 10.1088/1742-6596/1008/1/012011.

[5] Y. Al Amrani, M. Lazaar, and K. E. El Kadiri, “Random Forest and Support Vector Machine based Hybrid Approach to Sentiment Analysis,” Procedia Comput. Sci., vol. 127, pp. 511–520, Jan. 2018, doi: 10.1016/j.procs.2018.01.150.

[6] N. K. Bolbol and A. Y. Maghari, “Sentiment Analysis of Arabic Tweets Using Supervised Machine Learning,” in 2020 International Conference on Promising Electronic Technologies (ICPET), Dec. 2020, pp. 89–93, doi: 10.1109/ICPET51420.2020.00025.

[7] B. Noori, “Classification of Customer Reviews Using Machine Learning Algorithms,” Appl. Artif. Intell., vol. 35, no. 8, pp. 567–588, Jul. 2021, doi: 10.1080/08839514.2021.1922843.

[8] M. Khan and K. Malik, “Sentiment Classification of Customer’s Reviews About Automobiles in Roman Urdu,” in Advances in Intelligent Systems and Computing, vol. 887, Springer Verlag, 2019, pp. 630–640, doi: 10.1007/978-3-030-03405-4_44.

[9] M. A. Qureshi et al., “Sentiment Analysis of Reviews in Natural Language: Roman Urdu as a Case Study,” IEEE Access, vol. 10, pp. 24945–24954, 2022, doi: 10.1109/ACCESS.2022.3150172.

[10] S. Goyal, “Review Paper on Sentiment Analysis of Twitter Data Using Text Mining and Hybrid Classification Approach,” Internatio nal J. Eng. Dev. Res., vol. 5, no. 2, pp. 2321–9939, 2017, [Online]. Available: https://www.ijedr.org/papers/IJEDR1702032.pdf.

[11] A. Bayhaqy, S. Sfenrianto, K. Nainggolan, and E. R. Kaburuan, “Sentiment Analysis about E-Commerce from Tweets Using Decision Tree, K-Nearest Neighbor, and Naïve Bayes,” in 2018 International Conference on Orange Technologies (ICOT), Oct. 2018, pp. 1–6, doi: 10.1109/ICOT.2018.8705796.

[12] P. Cen, K. Zhang, and D. Zheng, “Sentiment Analysis Using Deep Learning Approach,” J. Artif. Intell., vol. 2, no. 1, pp. 17–27, Jul. 2020, doi: 10.32604/jai.2020.010132.

[13] S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput., vol. 9, no. 8, pp. 1735–1780, Nov. 1997, doi: 10.1162/neco.1997.9.8.1735.

[14] Q. Lu, “Neural Network based Approaches for Aspect-Based Sentiment Analysis,” Highlights Sci. Eng. Technol., vol. 12, pp. 222–229, Aug. 2022, doi: 10.54097/hset.v12i.1457.

[15] H. Li, Y. Ma, Z. Ma, and H. Zhu, “Weibo Text Sentiment Analysis Based on BERT and Deep Learning,” Appl. Sci., vol. 11, no. 22, p. 10774, Nov. 2021, doi: 10.3390/app112210774.

[16] N. S. Tun, N. N. Long, T. Tran, N. T. Thao, D. T. Thu Phuong, and T. Nguyen, “Stock article title sentiment-based classification using PhoBERT,” in CEUR Workshop Proceedings, 2021, vol. 3026, pp. 225–233.[Online]. Available at: https://ceur-ws.org/Vol-3026/paper25.pdf.

[17] Q. T. Nguyen, T. L. Nguyen, N. H. Luong, and Q. H. Ngo, “Fine-Tuning BERT for Sentiment Analysis of Vietnamese Reviews,” in 2020 7th NAFOSTED Conference on Information and Computer Science (NICS), Nov. 2020, pp. 302–307, doi: 10.1109/NICS51282.2020.9335899.

[18] T.-L. Truong, H.-L. Le, and T.-P. Le-Dang, “Sentiment Analysis Implementing BERT-based Pre-trained Language Model for Vietnamese,” in 2020 7th NAFOSTED Conference on Information and Computer Science (NICS), Nov. 2020, pp. 362–367, doi: 10.1109/NICS51282.2020.9335912.

[19] W. Zhang, X. Li, Y. Deng, L. Bing, and W. Lam, “A Survey on Aspect-Based Sentiment Analysis: Tasks, Methods, and Challenges,” in IEEE Transactions on Knowledge and Data Engineering, 2022, pp. 1–20, doi: 10.1109/TKDE.2022.3230975.

[20] A. S. Shafie, N. M. Sharef, M. A. Azmi Murad, and A. Azman, “Aspect Extraction Performance with POS Tag Pattern of Dependency Relation in Aspect-based Sentiment Analysis,” in 2018 Fourth International Conference on Information Retrieval and Knowledge Management (CAMP), Mar. 2018, pp. 1–6, doi: 10.1109/INFRKM.2018.8464692.

[21] M. Hu and B. Liu, “Mining and summarizing customer reviews,” in Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, Aug. 2004, pp. 168–177, doi: 10.1145/1014052.1014073.

[22] T. A. Rana and Y.-N. Cheah, “Exploiting sequential patterns to detect objective aspects from online reviews,” in 2016 International Conference On Advanced Informatics: Concepts, Theory And Application (ICAICTA), Aug. 2016, pp. 1–5, doi: 10.1109/ICAICTA.2016.7803101.

[23] K. Srividya and A. M. S. Sowjanya, “Aspect Based Sentiment Analysis using POS Tagging and TFIDF,” Int. J. Eng. Adv. Technol., vol. 8, no. 6, pp. 1960–1963, Aug. 2019, doi: 10.35940/ijeat.F7935.088619.

[24] L. Mai and B. Le, “Aspect-Based Sentiment Analysis of Vietnamese Texts with Deep Learning,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10751 LNAI, Springer Verlag, 2018, pp. 149–158, doi: 10.1007/978-3-319-75417-8_14.

[25] L. Luc Phan et al., “SA2SL: From Aspect-Based Sentiment Analysis to Social Listening System for Business Intelligence,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12816 LNAI, Springer Science and Business Media Deutschland GmbH, 2021, pp. 647–658, doi: 10.1007/978-3-030-82147-0_53.

[26] B. Liu, “Many Facets of Sentiment Analysis,” in A Partical Guide to Sentiment Analysis, Springer, Cham, 2017, pp. 11–39, doi: 10.1007/978-3-319-55394-8_2.

[27] T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient Estimation of Word Representations in Vector Space,” in 1st International Conference on Learning Representations, ICLR 2013 - Workshop Track Proceedings, Jan. 2013, pp. 1–12, doi: 10.48550/arXiv.1301.3781.

[28] J. Pennington, R. Socher, and C. Manning, “Glove: Global Vectors for Word Representation,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014, pp. 1532–1543, doi: 10.3115/v1/D14-1162.

[29] M. M. Rahman, Y. Watanobe, and K. Nakamura, “A Bidirectional LSTM Language Model for Code Evaluation and Repair,” Symmetry (Basel)., vol. 13, no. 2, p. 247, Feb. 2021, doi: 10.3390/sym13020247.

[30] T. O’Malley, E. Bursztein, J. Long, F. Chollet, H. Jin, and L. Invernizzi, “A Hyperparameter Tuning Library for Keras,” GitHub, 2019. Accessed Apr. 30, 2020. [Online]. Available at: https://github.com/keras-team/keras-tuner.

[31] B.-T. Nguyen-Thi and H.-T. Duong, “A Vietnamese Sentiment Analysis System Based on Multiple Classifiers with Enhancing Lexicon Features,” in Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 293, Springer Verlag, 2019, pp. 240–249, doi: 10.1007/978-3-030-30149-1_20.

[32] D. R. Cox, “The Regression Analysis of Binary Sequences,” J. R. Stat. Soc. Ser. B, vol. 20, no. 2, pp. 215–232, Jul. 1958, doi: 10.1111/j.2517-6161.1958.tb00292.x.

[33] D. P. Kingma and J. L. Ba, “Adam: A Method for Stochastic Optimization,” 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings. International Conference on Learning Representations, ICLR, pp. 1–15, Dec. 22, 2014, doi: 10.48550/arXiv.1412.6980.




Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

___________________________________________________________
International Journal of Advances in Intelligent Informatics
ISSN 2442-6571  (print) | 2548-3161 (online)
Organized by UAD and ASCEE Computer Society
Published by Universitas Ahmad Dahlan
W: http://ijain.org
E: info@ijain.org (paper handling issues)
   andri.pranolo.id@ieee.org (publication issues)

View IJAIN Stats

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0