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Vol. 10, No. 1, February 2025

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Aspect-based Multilabel Classification of E-commerce Reviews using Fine-tuned IndoBERT

https://doi.org/10.22219/kinetik.v10i1.2088
Fahrendra Khoirul Ihtada
Maulana Malik Ibrahim State Islamic University
Rizha Alfianita
Maulana Malik Ibrahim State Islamic University
Okta Qomaruddin Aziz
Maulana Malik Ibrahim State Islamic University

Corresponding Author(s) : Okta Qomaruddin Aziz

okta.qomaruddin@uin-malang.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 10, No. 1, February 2025

Abstract

In recent years, e-commerce has experienced rapid growth. A significant change in consumer behavior is marked by the ease of access and time flexibility offered by e-commerce platforms, as well as the existence of the review feature to assess products and services. However, with the ever-increasing number of reviews, consumers and store owners face challenges in sorting out relevant information. This research focuses on the multilabel classification of Indonesian e-commerce reviews. This research was undertaken because the application of multilabel classification, especially for e-commerce reviews in Indonesia, has received little attention. This research compares three classification models: end-to-end IndoBERT, IndoBERT-CNN, and IndoBERT-LSTM, to determine the most effective model for multilabel aspect classification of customer reviews. The multilabel classification method was applied to determine the aspect categories of the reviews, such as product, customer service, and delivery, using different thresholds for evaluation. Results show that 0.6 threshold is optimal, with the IndoBERT-LSTM model as the best-performing model for the multilabel aspect classification of these e-commerce reviews. Optimal classification of the model enables more precise information extraction from customer reviews. This can be useful for e-commerce businesses to gain insight from the reviews they get from customers. This insight can be used to find out which aspects need to be improved from the e-commerce business which leads to increased customer satisfaction and trust.

Keywords

Aspect category Multilabel classification E-commerce reviews IndoBERT
Ihtada, F. K., Alfianita, R., & Aziz, O. Q. (2025). Aspect-based Multilabel Classification of E-commerce Reviews using Fine-tuned IndoBERT. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 10(1). https://doi.org/10.22219/kinetik.v10i1.2088
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References
  1. T. K. Lestari, A. L. Kusumatrisna, and A. Syakilah, Statistik E-commerce 2021. BPS-Statistics Indonesia, 2021.
  2. T. Chen, P. Samaranayake, X. Y. Cen, M. Qi, and Y. C. Lan, “The Impact of Online Reviews on Consumers’ Purchasing Decisions: Evidence from an Eye-Tracking Study,” Front Psychol, vol. 13, Jun. 2022. https://doi.org/10.3389/fpsyg.2022.865702
  3. D. Yanti, N. Kristya Ningsih, J. G. Ony, and S. P. Suhalim, “The Influence of Online Customer Reviews and Online Customer Ratings on Product Purchase Decisions on The Tokopedia Marketplace,” Jurnal Pemasaran Kompetitif, vol. 07, no. 2, p. 2024. https://doi.org/10.32493/jpkpk.v7i2.40083
  4. Y. Gurav, V. Ingawale, and A. Yadav, “A Study on The Impact Of Online Product Reviews On Consumers’ Buying Intentions,” The Online Journal of Distance Education and e-Learning, vol. 11, no. 2, pp. 1924–1928, 2023.
  5. S. Imron, E. I. Setiawan, and J. Santoso, “Deteksi Aspek Review E-Commerce Menggunakan IndoBERT Embedding dan CNN,” Journal of Intelligent System and Computation, vol. 5, no. 1, pp. 10–16, Apr. 2023. https://doi.org/10.52985/insyst.v5i1.267
  6. D. F. Nasiri and I. Budi, “Aspect Category Detection on Indonesian E-commerce Mobile Application Review,” in 2019 International Conference on Data and Software Engineering (ICoDSE), Institute of Electrical and Electronics Engineers Inc., Nov. 2019. https://doi.org/10.1109/ICoDSE48700.2019.9092619
  7. A. Lunardi, J. Viterbo, C. Boscarioli, F. Bernardini, and C. Maciel, “Domain-tailored Multiclass Classification of User Reviews based on Binary Splits,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, 2016, pp. 298–309. https://doi.org/10.1007/978-3-319-39910-2_28
  8. E. Deniz, H. Erbay, and M. Coşar, “Multi-Label Classification of E-Commerce Customer Reviews via Machine Learning,” Axioms, vol. 11, no. 9, p. 436, Sep. 2022. https://doi.org/10.3390/axioms11090436
  9. G. Khlifi, I. Jenhani, M. Ben Messaoud, and M. W. Mkaouer, “Multi-label Classification of Mobile Application User Reviews Using Neural Language Models,” in Symbolic and Quantitative Approaches to Reasoning with Uncertainty, Z. Bouraoui and S. Vesic, Eds., Cham: Springer Nature Switzerland, 2024, pp. 417–426. https://doi.org/10.1007/978-3-031-45608-4_31
  10. M. Abubakar, A. Shahzad, H. Abbasi, and I. Abbottabad Campus Pakistan, “Aspect-Based Sentiment Analysis on Amazon Product Reviews,” International Journal of Informatics Information System and Computer Engineering, vol. 2, no. 2, pp. 206–211, 2021. https://doi.org/10.34010/injiiscom.v2i2.7455
  11. G. Liu, S. Fei, Z. Yan, C. H. Wu, and S. B. Tsai, “An Empirical Study on Response to Online Customer Reviews and E-Commerce Sales: From the Mobile Information System Perspective,” Mobile Information Systems, vol. 2020, no. 1, 2020. https://doi.org/10.1155/2020/8864764
  12. P. Ravula, “Impact of Delivery Performance on Online Review Ratings: The Role of Temporal Distance of Ratings,” Journal of Marketing Analytics, vol. 11, no. 2, pp. 149–159, Jun. 2023. https://doi.org/10.1057/s41270-022-00168-5
  13. Y. Liu et al., “RoBERTa: A Robustly Optimized BERT Pretraining Approach,” Jul. 2019. https://doi.org/10.48550/arXiv.1907.11692
  14. Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R. Salakhutdinov, and Q. V. Le, “XLNet: Generalized Autoregressive Pretraining for Language Understanding,” Jun. 2019. https://doi.org/10.48550/arXiv.1906.08237
  15. N. K. Nissa and E. Yulianti, “Multi-label Text Classification of Indonesian Customer Reviews Using Bidirectional Encoder Representations from Transformers Language Model,” International Journal of Electrical and Computer Engineering, vol. 13, no. 5, pp. 5641–5652, Oct. 2023. https://doi.org/10.11591/ijece.v13i5.pp5641-5652
  16. I. Akbar, M. Faisal, and T. Chamidy, “Multi-label Classification of Indonesian Qur’an Translation using Long Short-Term Memory Model,” Computer Network, Computing, Electronics, and Control Journal, vol. 9, no. 2, pp. 119–128, 2019. https://doi.org/10.22219/kinetik.v9i2.1901
  17. J. Forry Kusuma and A. Chowanda, “Indonesian Hate Speech Detection Using IndoBERTweet and BiLSTM on Twitter,” JOIV : International Journal on Informatic Visualization, vol. 7, no. 3, pp. 773–780, 2023. https://dx.doi.org/10.30630/joiv.7.3.1035
  18. B. Wilie et al., “IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding,” Sep. 2020. https://doi.org/10.18653/v1/2020.aacl-main.85
  19. B. V. Kartika, M. J. Alfredo, and G. P. Kusuma, “Fine-Tuned IndoBERT based model and data augmentation for indonesian language paraphrase identification,” Revue d’Intelligence Artificielle, vol. 37, no. 3, pp. 733–743, Jun. 2023. https://doi.org/10.18280/ria.370322
  20. G. Z. Nabiilah, I. N. Alam, E. S. Purwanto, and M. F. Hidayat, “Indonesian multilabel classification using IndoBERT embedding and MBERT classification,” International Journal of Electrical and Computer Engineering, vol. 14, no. 1, pp. 1071–1078, Feb. 2024. https://doi.org/10.11591/ijece.v14i1.pp1071-1078
  21. H. Tanaka, H. Shinnou, R. Cao, J. Bai, and W. Ma, “Document Classification by Word Embeddings of BERT,” in Communications in Computer and Information Science, Springer, 2020, pp. 145–154. https://doi.org/10.1007/978-981-15-6168-9_13
  22. Hugging Face, “IndoBERT: indobenchmark/indobert-base-p1.” Accessed: Oct. 14, 2024.
  23. X. Zhao, L. Wang, Y. Zhang, X. Han, M. Deveci, and M. Parmar, “A Review of Convolutional Neural Networks in Computer Vision,” Artif Intell Rev, vol. 57, no. 4, Apr. 2024. https://doi.org/10.1007/s10462-024-10721-6
  24. W. K. Sari, D. P. Rini, and R. F. Malik, “Text Classification Using Long Short-Term Memory With GloVe Features,” Jurnal Ilmiah Teknik Elektro Komputer dan Informatika, vol. 5, no. 2, p. 85, Feb. 2020. https://doi.org/10.26555/jiteki.v5i2.15021
  25. Z. Xingfu, H. Gweon, and S. Provost, “Threshold Moving Approaches for Addressing the Class Imbalance Problem and their Application to Multi-label Classification,” in Proceedings of 2020 the 4th International Conference on Advances in Image Processing (ICAIP 2020), 2020, pp. 72–75. https://doi.org/10.1145/3441250.3441274
  26. C. Murphy, J. A. Tawn, and Z. Varty, “Automated Threshold Selection and Associated Inference Uncertainty for Univariate Extremes,” Oct. 2023. https://doi.org/10.1080/00401706.2024.2421744
  27. A. Rofiqul Muslikh, I. Akbar, D. Rosal Ignatius Moses Setiadi, and H. Md Mehedul Islam, “Multi-label Classification of Indonesian Al-Quran Translation based CNN, BiLSTM, and FastText,” Techno.COM, vol. 23, no. 1, pp. 37–50, 2024. https://doi.org/10.62411/tc.v23i1.9925
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