Aspect-Based Sentiment Analysis of Fashion and Apparel Related Reviews using Transformer Model
Keywords:
Natural language processing, Aspect-based sentiment analysis, Fashion and Apparel, E-commerce, Customer reviews, Consumer insightsAbstract
The increasing reliance on e-commerce has led to a surge in customer-generated content, making sentiment analysis a crucial tool for understanding consumer preferences. This study presents a novel aspect-based sentiment analysis (ABSA) approach to extract fine-grained insights from customer reviews across multiple product categories, including lingerie, women’s clothing, men’s clothing, kids’ apparel, home & furniture, and flowers & plants. We employ exploratory data analysis (EDA) to uncover key patterns in customer sentiment and utilize a transformer-based model with contrastive learning to enhance sentiment classification accuracy. Our approach effectively captures sentiment variations across nine different aspects, including quality, value for money, style, fit, material, warmth, comfort, support, and how well the product fits. The results demonstrate that our model outperforms traditional sentiment analysis methods, offering a more structured understanding of customer feedback. By analyzing customer sentiments from reviews, businesses can identify dissatisfaction patterns that may contribute to customer churn. Addressing these issues proactively can help improve customer retention and loyalty. These insights can help businesses refine their product offerings and improve customer satisfaction across diverse e-commerce categories.
Downloads
References
1. Huang H, Zavareh AA, Mustafa MB. Sentiment analysis in e-commerce platforms: A review of current techniques and future directions. IEEE Access. 2023;11:90367-82. doi: 10.1109/ACCESS.2023.3307308.
2. Badmus O, Rajput SA, Arogundade JB, Williams M. AI-driven business analytics and decision making. World Journal of Advanced Research and Reviews. 2024;24(1):616-33. doi: 10.30574/wjarr.2024.24.1.3093.
3. Semwal R, Tripathi N, Pandey UK. Leveraging artificial intelligence in service marketing to enhance electronic word of mouth (eWOM). AI Innovations in Service and Tourism Marketing: IGI Global; 2024. p. 249-69.
4. Unalp A. AI-powered business insights: Leveraging data mining for strategic market analysis. 2024.
5. Karunarathne PDAI, Wanninayake WMCB. Impact of Social Media Marketing on Brand Loyalty in Online Fashion Retail Industry in Sri Lanka (SDG 09): The Mediating Role of Brand Consciousness. JLSDGR. 2025;5(8):e6049. doi: 10.47172/2965-730x.sdgsreview.v5.n08.pe06049.
6. Radha DS. Role of Artificial Intelligence in Shaping Consumer Experience and Loyalty in Fashion E-Commerce. Interantional Journal of Scientific Research in Engineering and Management. 2025;09(04):1-9. doi: 10.55041/ijsrem44114.
7. Agrawal K. AI-enhanced Intelligent Fashion eCommerce: Virtual Try-on and Personalized Style Recommendations in Action. World Journal of Advanced Engineering Technology and Sciences. 2025;15(1):2142-50. doi: 10.30574/wjaets.2025.15.1.0475.
8. Tam FY, Lung J. Digital marketing strategies for luxury fashion brands: A systematic literature review. International Journal of Information Management Data Insights. 2025;5(1):100254. doi: 10.1016/j.jjimei.2024.100309.
9. Liu B. Sentiment analysis and opinion mining: Springer Nature; 2022.
10. Wankhade M, Kulkarni C, Rao ACS. A survey on aspect base sentiment analysis methods and challenges. Applied Soft Computing. 2024:112249. doi: 10.1016/j.asoc.2024.112249.
11. Zhang W, Li X, Deng Y, Bing L, Lam W. A survey on aspect-based sentiment analysis: Tasks, methods, and challenges. IEEE Transactions on Knowledge and Data Engineering. 2022;35(11):11019-38. doi: 10.1109/TKDE.2022.3230975.
12. Perikos I, Diamantopoulos A. Explainable aspect-based sentiment analysis using transformer models. Big Data and Cognitive Computing. 2024;8(11):141. doi: 10.3390/bdcc8110141.
13. Bellar O, Baina A, Ballafkih M. Sentiment analysis: Predicting product reviews for e-commerce recommendations using deep learning and transformers. Mathematics. 2024;12(15):2403. doi: 10.3390/math12152403.
14. Prince NU, Shawon MNH, Shahed ZK, Nupur RI, Mele FA, Al Mamun MA. E-commerce clothing review analysis by advanced ML algorithms. 2024. doi: 10.30574/wjarr.2024.24.1.3044.
15. Sinap V. Classification of customer sentiments based on online reviews: Comparative analysis of machine learning and deep learning algorithms. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi. 2024;27(3):779-99. doi: 10.17780/ksujes.1420530.
16. Ramshankar N, Pm JP. Automated sentimental analysis using heuristic-based CNN-BiLSTM for e-commerce dataset. Data & Knowledge Engineering. 2023;146:102194. doi: 10.1016/j.datak.2023.102194.
17. Shetty AM, Aljunid MF, Manjaiah D. Sentiment exploring on feedback of e-commerce data using machine learning algorithms. International Conference on Emerging Research in Computing, Information, Communication and Applications2023.
18. Taneja K, Vashishtha J, Ratnoo S. Transformer based unsupervised learning approach for imbalanced text sentiment analysis of e-commerce reviews. Procedia Computer Science. 2024;235:2318-31. doi: 10.1016/j.procs.2024.04.220.
19. Consoli S, Barbaglia L, Manzan S. Fine-grained, aspect-based sentiment analysis on economic and financial lexicon. Knowledge-Based Systems. 2022;247:108781. doi: 10.1016/j.knosys.2022.108781.
20. Ong K, Van Der Heever W, Satapathy R, Cambria E, Mengaldo G, editors. FinXABSA: Explainable finance through aspect-based sentiment analysis. 2023 IEEE International Conference on Data Mining Workshops (ICDMW); 2023.
21. Kabiito D, Nakatumba-Nabende J. Targeted aspect-based sentiment analysis for Ugandan telecom reviews from Twitter. Advances in Artificial Intelligence and Applied Cognitive Computing. Cham: Springer; 2021.
22. Tesfa T, Belete B, Abera S, Mohapatra SK, Das TK, editors. Aspect-based sentiment analysis on Amharic text for evaluating Ethio-telecom services. 2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE); 2024.
23. Zhang Y, Lu H, Jiang C, Li X, Tian X. Aspect-based sentiment analysis of user reviews in 5G networks. IEEE Network. 2021;35(4):228-33. doi: 10.1109/MNET.011.2000400.
24. AlNasser S, AlMuhaideb S. Listening to patients: Advanced Arabic aspect-based sentiment analysis using transformer models towards better healthcare. Big Data and Cognitive Computing. 2024;8(11):156. doi: 10.3390/bdcc8110156.
25. Rani S, Jain A. Aspect-based sentiment analysis of drug reviews using multi-task learning based dual BiLSTM model. Multimedia Tools and Applications. 2024;83(8):22473-501. doi: 10.1007/s11042-023-16360-3.
26. Carrasco P, Dias S. Enhancing restaurant management through aspect-based sentiment analysis and NLP techniques. Procedia Computer Science. 2024;237:129-37. doi: 10.1016/j.procs.2024.05.088.
27. Shahhosseini M, Khalili Nasr A. What attributes affect customer satisfaction in green restaurants? An aspect-based sentiment analysis approach. Journal of Travel & Tourism Marketing. 2024;41(4):472-90. doi: 10.1080/10548408.2024.2306358.
28. Ozen IA, Ozgul Katlav E. Aspect-based sentiment analysis on online customer reviews: A case study of technology-supported hotels. Journal of Hospitality and Tourism Technology. 2023;14(2):102-20. doi: 10.1108/JHTT-12-2020-0319.
29. Savci P, Das B. Prediction of the customers' interests using sentiment analysis in e-commerce data for comparison of Arabic, English, and Turkish languages. Journal of King Saud University - Computer and Information Sciences. 2023;35(3):227-37. doi: 10.1016/j.jksuci.2023.02.017.
30. Huda I, Suhendra AA, Bijaksana MA. Design of prediction model using data mining for segmentation and classification customer churn in e-commerce mall in mall. JOIV: International Journal on Informatics Visualization. 2023;7(4):2280-9. doi: 10.62527/joiv.7.4.2414.
31. Manzoor A, Qureshi MA, Kidney E, Longo L. A review on machine learning methods for customer churn prediction and recommendations for business practitioners. IEEE Access. 2024. doi: 10.1109/ACCESS.2024.3402092.
32. Singhal M. Customer relationship management strategy for customer retention and acquisition. Pen Acclaims. 2021;14(1).
33. Li R, Chen H, Feng F, Ma Z, Wang X, Hovy E, editors. Dual graph convolutional networks for aspect-based sentiment analysis. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing; 2021.
34. Prakash Y, Sharma DK, editors. Aspect based sentiment analysis for Amazon data products using PAM. 2023 6th International Conference on Information Systems and Computer Networks (ISCON); 2023.
35. Alanazi MSM, Li J, Jenkins KW. Multiclass sentiment prediction of airport service online reviews using aspect-based sentimental analysis and machine learning. Mathematics. 2024;12(5):781. doi: 10.3390/math12050781.
Downloads
Published
Submitted
Revised
Accepted
Issue
Section
License
Copyright (c) 2025 Foud Ameri (Author); Ghasem Bakhshandeh; Maryam Darvishi (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
