A Machine-Learning-Inspired Opinion Extraction Mechanism for Classifying Customer Reviews on Social Media
Source abstract
Abstract
Machine learning frameworks categorizing customer reviews on online products have significantly improved sales and product quality for major manufacturers. Manually scrutinizing extensive customer reviews is imprecise and time-consuming. Current product research techniques rely on text mining, neglecting audio, and image components, resulting in less productive outcomes for researchers and developers. AI-based machine learning frameworks that consider social media and online buyer reviews are essential for accurate recommendations in online e-commerce shops. This research paper proposes a novel machine-learning-based framework for categorizing customer reviews that uses a bag-of-features approach for feature extraction and a hybrid DNN framework for robust classification. We assess the performance of our machine learning framework using AliExpress and Amazon e-commerce product review data provided by customers, and we have achieved a classification accuracy of 91.5% with only 8.46% fallout. Moreover, when compared with state-of-the-art models, our proposed model shows superior performance in terms of sensitivity, specificity, precision, fallout, and accuracy.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Sentiment Analysis and Opinion Mining · Text and Document Classification Technologies · Spam and Phishing Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Information Systems, Faculty of Computing and Information Technology (FCIT), King Abdulaziz University Jeddah, Jeddah 34025, Saudi Arabia
Funding
institutional Fund Projects · Ministry of Education and King Abdul Aziz University, DSR, Jeddah, Saudi Arabia
References (40)
Show source-provided references
1. Fülöp, M.T., Topor, D.I., Căpușneanu, S., Ionescu, C.A., and Akram, U. (2023). Utilitarian and Hedonic Motivation in E-Commerce Online Purchasing Intentions. East. Eur. Econ., 1–23.10.1080/00128775.2023.2197878 ↗
2. Akram, U., Fülöp, M.T., Tiron-Tudor, A., Topor, D.I., and Căpușneanu, S. (2021). Impact of digitalization on customers’ well-being in the pandemic period: Challenges and opportunities for the retail industry. Int. J. Environ. Res. Public Health, 18.10.3390/ijerph18147533 ↗
3. Creating and detecting fake reviews of online products10.1016/j.jretconser.2021.102771 ↗
4. EXPRS: An extended pagerank method for product feature extraction from online consumer reviews10.1016/j.im.2015.02.002 ↗
5. Egger, R., and Gokce, E. (2022). Natural Language Processing (NLP): An Introduction: Making Sense of Textual Data, Springer.10.1007/978-3-030-88389-8_15 ↗
6. Srivastava, S., Verma, A., and Sharma, S. (2022, January 19–20). Optical character recognition techniques: A review. Proceedings of the 2022 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), Bhopal, India.10.1109/SCEECS54111.2022.9740911 ↗
7. Paying Lip Service?: The Effects of Vocal Determinants on Perceived Service Quality10.4018/IJEIS.313049 ↗
8. Food and Drug Administration (2021, May 03). Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD). Available online: https://apo.org.au/node/228371.
9. Stanik, C., Haering, M., and Maalej, W. (2019, January 23–27). Classifying multilingual user feedback using traditional machine learning and deep learning. Proceedings of the 2019 IEEE 27th International Requirements Engineering Conference Workshops (REW), Jeju, Republic of Korea.10.1109/REW.2019.00046 ↗
10. Data science for building energy efficiency: A comprehensive text-mining driven review of scientific literature10.1016/j.enbuild.2021.110885 ↗
11. Natural language processing for smart construction: Current status and future directions10.1016/j.autcon.2021.104059 ↗
12. Wang, J., Yang, Z., Hu, X., Li, L., Lin, K., Gan, Z., Liu, Z., Liu, C., and Wang, L. (2022). Git: A generative image-to-text transformer for vision and language. arXiv.
13. Texture representation through overlapped multi-oriented tri-scale local binary pattern10.1109/ACCESS.2019.2918004 ↗
14. Robustness-driven hybrid descriptor for noise-deterrent texture classification10.1109/ACCESS.2019.2932687 ↗
15. Person re-identification by discriminative local features of overlapping stripes10.3390/sym12040647 ↗
16. BLPnet: A new DNN model and Bengali OCR engine for Automatic Licence Plate Recognition10.1016/j.array.2022.100244 ↗
17. Advances in subword-based HMM-DNN speech recognition across languages10.1016/j.csl.2020.101158 ↗
18. Kamble, P.M., Hegadi, R.S., and Hegadi, R.S. (2018, January 21–22). Distance based edge linking (DEL) for character recognition. Proceedings of the Recent Trends in Image Processing and Pattern Recognition: Second International Conference, RTIP2R 2018, Solapur, India.10.1007/978-981-13-9187-3_23 ↗
19. Rabbi, K.K., Hossain, A., Dev, P., Sadman, A., Karim, D.Z., and Rasel, A.A. (2022, January 17–19). KDANet: Handwritten Character Recognition for Bangla Language using Deep Learning. Proceedings of the 2022 25th International Conference on Computer and Information Technology (ICCIT), Cox’s Bazar, Bangladesh.10.1109/ICCIT57492.2022.10054708 ↗
20. The text mining from online customer reviews: Implications for luxury hotel in Busan
21. Fuzzy bag-of-words model for document representation10.1109/TFUZZ.2017.2690222 ↗
22. Paul, D., Shifas, M.P., Pantazis, Y., and Stylianou, Y. (2020). Enhancing speech intelligibility in text-to-speech synthesis using speaking style conversion. arXiv.10.21437/Interspeech.2020-2793 ↗
23. Ingle, R.R., Fujii, Y., Deselaers, T., Baccash, J., and Popat, A.C. (2019, January 20–25). A scalable handwritten text recognition system. Proceedings of the 2019 International Conference on Document Analysis and Recognition (ICDAR), Sydney, NSW, Australia.10.1109/ICDAR.2019.00013 ↗
24. Backdoor attacks against transfer learning with pre-trained deep learning models10.1109/TSC.2020.3000900 ↗
25. Texture analysis and its applications in biomedical imaging: A survey10.1109/RBME.2021.3115703 ↗
26. Speech emotion recognition based on SVM and KNN classifications fusion
27. Kernel sparse representation-based classifier ensemble for face recognition10.1007/s11042-013-1457-1 ↗
28. Lee, W.J., Verzakov, S., and Duin, R.P. (2007, January 23–25). Kernel combination versus classifier combination. Proceedings of the Multiple Classifier Systems: 7th International Workshop, MCS 2007, Prague, Czech Republic.
29. Kernel-based methods for hyperspectral image classification10.1109/TGRS.2005.846154 ↗
30. Text feature extraction based on deep learning: A review10.1186/s13638-017-0993-1 ↗
31. The impact of features extraction on the sentiment analysis10.1016/j.procs.2019.05.008 ↗
32. Gasparetto, A., Marcuzzo, M., Zangari, A., and Albarelli, A. (2022). A survey on text classification algorithms: From text to predictions. Information, 13.10.3390/info13020083 ↗
33. Fake reviews detection: A survey10.1109/ACCESS.2021.3075573 ↗
34. The Lao text classification method based on KNN10.1016/j.procs.2020.02.053 ↗
35. E-commerce product review sentiment classification based on a naïve Bayes continuous learning framework10.1016/j.ipm.2020.102221 ↗
36. A gradient boosted decision tree-based sentiment classification of twitter data10.1142/S0219691320500277 ↗
37. Haralabopoulos, G., and Anagnostopoulos, I. (2022, January 17–20). A Custom State LSTM Cell for Text Classification Tasks. Proceedings of the Engineering Applications of Neural Networks: 23rd International Conference, EAAAI/EANN 2022, Chersonissos, Crete, Greece.10.1007/978-3-031-08223-8_40 ↗
38. Stacked residual recurrent neural networks with cross-layer attention for text classification10.1109/ACCESS.2020.2987101 ↗
39. Li, A., Qin, Z., Liu, R., Yang, Y., and Li, D. (2019, January 3–7). Spam review detection with graph convolutional networks. Proceedings of the 28th ACM International Conference on Information and Knowledge Management, Beijing, China.10.1145/3357384.3357820 ↗
40. Deniz, E., Erbay, H., and Coşar, M. (2022). Multi-label classification of e-commerce customer reviews via machine learning. Axioms, 11.10.3390/axioms11090436 ↗