Classification of Vietnamese Reviews on E-Commerce Platforms
Main Article Content
Abstract
The research team used machine learning models to classify Vietnamese reviews on products on the e-commerce platform as positive or negative. To classify and evaluate the effectiveness of Support Vector Machine (SVM), Random Forest, Logistic Regression machine learning models on different platforms, the authors have built their own training and test data sets as well as a set of stopwords to classify Vietnamese web reviews [9]. This can then be applied to building a webapp that allows entering a link of any online products and then categorizing its user reviews, helping sellers evaluate their products/services, understand consumer behavior and make changes, improvements to the products accordingly.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
How to Cite
References
Fradkin, Dmitriy; Muchnik, Ilya (2006). "Support Vector Machines for Classification” Discrete Methods in Epidemiology. DIMACS Series in Discrete Mathematics and Theoretical Computer Science. Vol. 70. pp. 13–20
Ho, Tin Kam (1995). Random Decision Forests Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, 14–16 August 1995.
Joachims T., “Text categorization with Support Vector Machines: Learning with many relevant features”, in Proc. of the European Conference on Machine Learning (ECML), 1998, pages 137–142. https://doi.org/10.1007/BFb0026683
K. J. Piczak, "Environmental sound classification with convolutional neural networks," in Proceedings of the IEEE 25th International Workshop on Machine Learning for Signal Processing, 2015, pp. 1-6. https://doi.org/10.1109/MLSP.2015.7324337
Prinzie, A.; Van den Poel, D. (2008). "Random Forests for multiclass classification: Random MultiNomial Logit". Expert Systems with Applications. 34 (3): 1721–1732. https://doi.org/10.1016/j.eswa.2007.01.029
Tolles, Juliana; Meurer, William J (2016). "Logistic Regression Relating Patient Characteristics to Outcomes". JAMA. 316 (5): 533–4. https://doi.org/10.1001/jama.2016.7653
Venkatesan, Ragav; Li, Baoxin (2017-10-23). Convolutional Neural Networks in Visual Computing: A Concise Guide. CRC Press. ISBN 978-1-351-65032-8. Archived from the original on 2023-10-16. Retrieved 2020-12-13.
https://underthesea.readthedocs.io/en/latest/readme.html
Thegioididong.com
Sistla, S. (2022). Predicting Diabetes u sing SVM Implemented by Machine Learning. In International Journal of Soft Computing and Engineering (Vol. 12, Issue 2, pp. 16–18). https://doi.org/10.35940/ijsce.b3557.0512222
Muthukrishnan, Dr. R., & Prakash, N. U. (2023). Validate Model Endorsed for Support Vector Machine Alignment with Kernel Function and Depth Concept to Get Superlative Accurateness. In International Journal of Basic Sciences and Applied Computing (Vol. 9, Issue 7, pp. 1–5). https://doi.org/10.35940/ijbsac.g0486.039723
Nagar, K., & Chawla, M. P. S. (2023). A Survey on Various Approaches for Support Vector Machine Based Engineering Applications. In International Journal of Emerging Science and Engineering (Vol. 11, Issue 11, pp. 6–11). https://doi.org/10.35940/ijese.k2555.10111123
Sharma, D. S. K., & Sharma, M. N. K. (2019). Text Classification Using Ensemble Of Non-Linear Support Vector Machines. In International Journal of Innovative Technology and Exploring Engineering (Vol. 8, Issue 10, pp. 3169–3174). https://doi.org/10.35940/ijitee.j9520.0881019
Kumar, C. S., & Thangaraju, P. (2019). Improving Classifier Accuracy for diagnosing Chronic Kidney Disease Using Support Vector Machines. In International Journal of Engineering and Advanced Technology (Vol. 8, Issue 6, pp. 3697–3706). https://doi.org/10.35940/ijeat.f9377.088619