MODELS FOR IDENTIFYING CONSUMER OPINIONS USING DIGITAL TECHNOLOGIES IN E-COMMERCE ACTIVITIES
DOI:
https://doi.org/10.55640/Keywords:
consumer opinion; e commerce; digital technologies; sentiment analysis; opinion mining; machine learning; deep learning; topic modelling; user generated reviewsAbstract
In the environment of modern ecommerce, the ability to aggregate, interpret and act upon consumer opinions represents a strategic differentiator. This article explores models for identifying consumer opinions using digital technologies in ecommerce activities. Drawing on recent literature, it outlines key modeling approaches—ranging from rulebased sentiment lexicons to advanced deep neural networks—explores their methodological foundations, presents core results from empirical studies, and discusses implications for practice and future research. The findings suggest that integrated architectures combining naturallanguage processing (NLP), machinelearning classification, and digital trace analytics offer the strongest potential to convert raw consumer feedback into actionable insight.
References
1.Davoodi, L., Mezei, J. & Heikkilä, M. “Aspectbased sentiment classification of user reviews to understand customer satisfaction of ecommerce platforms.” Electronic Commerce Research (2025). [Proceedings].
2.Liu, Y., Lu, J., Yang, J. & Mao, F. “Sentiment analysis for ecommerce product reviews by deep learning model of BertBiGRUSoftmax.” Mathematical Biosciences and Engineering, 17(6): 78197837 (2020).
3.Haroon, M., Alam, Z., Kousar, R., Ahmad, J., Nasim, F. “Sentiment Analysis of Customer Reviews on ECommerce Platforms: A Machine Learning Approach.” Bulletin of Business and Economics, 13(3): 230238 (2025).
4.Banerjee, K. & Sarkar, S. “Sentiment analysis using different machine learning models: A study for the prediction of customer’s review.” Journal of Legal, Ethical and Regulatory Issues, 27(1): 110 (2024).
5.Lee, Y., Kim, J., Kim, D., Kho, Y., Kim, Y., Kang, P. “Painsight: An Extendable Opinion Mining Framework for Detecting Pain Points Based on Online Customer Reviews.” arXiv preprint (2023).
6.Ruder, S., Ghaffari, P., Breslin, J.G. “A Hierarchical Model of Reviews for Aspectbased Sentiment Analysis.” arXiv preprint (2016).
7.Singhal, R.K. “AIPowered Personalization in ECommerce: Consumer Perceptions, Trust and Purchase DecisionMaking.” ACR Journal (2025).
8.Sharma, P. “Emerging digital technologies and consumer decisionmaking in the retail sector.” Journal of Retailing & Consumer Services, (2023).
9.Ologunebi, J. “Digital Consumer Behavior in Ecommerce: A Study of Amazon and Temu’s Customer Purchase DecisionMaking Processes in the UK and USA.” MPRA Paper 123096 (2024).
10.Masfer, H.M. & Helmi, M.A. “The Role of Digital Marketing in Shaping Consumer Behavior in ECommerce Platforms.” International Journal of Professional Business Review, 10(3): 119 (2025).
11.Lim, S.F.W.T. “Consumerdriven ecommerce: A literature review, design and research agenda.” International Journal of Physical Distribution & Logistics Management, 48(3): 308?? (2018).
12.Handoyo, S. “Purchasing in the digital age: A metaanalytical perspective on ecommerce consumer decisionmaking.” PMC – Journal (2024).
13.“Opinion mining in ecommerce: Evaluating machine …” Elsevier (2025).
14.Atlas, L.G. et al. “A modernized approach to sentiment analysis of product reviews using BiGRU and RNN based LSTM deep learning models.” Scientific Reports, 15:16642 (2025).
15.Adanyin, A. “Ethical AI in Retail: Consumer Privacy and Fairness.” arXiv (2024).
16.Liu, B. “Deep Learning for Sentiment Analysis: A Survey.” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4): ??? (2018).
17.Pang, B., Lee, L., & Vaithyanathan, S. “Thumbs up? Sentiment classification using machine learning techniques.” EMNLP (2002). (cited in Liu et al. (2020) pp. 78207821)
18.“Analisi del sentiment” (Italian Wikipedia) provides overview of sentimentanalysis concept (2025).
19.“Elaboration likelihood model” (Wikipedia) — online explanation route of persuasion used in digital marketing context (2025).
20.“Multimodal sentiment analysis” (Wikipedia) — overview of extending sentiment analysis beyond text (2025).
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