Consumer Sentiment-Driven Product Ranking Using a Feature-Level Deep Learning Approach: The Case of New and Refurbished Laptops

Electronic waste (E-waste) is an escalating global challenge, with discarded laptops forming a major share of this growing environmental burden. To support sustainable consumption and informed consumer decision making, this study proposes an unsupervised deep learning framework that ranks refurbished and new laptop brands based on consumer sentiment extracted from online reviews. The framework identifies not only direct product features called aspects (such as battery, display, or customer support) but also experiential dimensions (such as reliability, performance, or overall satisfaction), providing a holistic view of consumer perception. By leveraging a transformer-based multi-headed attention mechanism and part-of-speech tagging, the model extracts rich five-part sentiment structures: Aspect/Dimension, Category, Opinion, Irrealis (hypotheticals), and Sentiment, collectively represented as ACOIS and DCOIS quintuples. These insights feed into a folksonomy-based consumer brand ranking (CBR) algorithm, which aggregates sentiment scores to rank laptop brands effectively. Unlike traditional models, this framework requires no labeled training data, increasing its adaptability across domains. Comparative evaluations against state-of-the-art supervised and self-supervised models, including Large Language Models (LLMs), demonstrate superior performance with F1 score improvements of 9%, 6%, and 4% in extracting product aspects, dimensions, and opinions, respectively. The model is applied to a curated dataset comprising new and refurbished laptops within the same price segment. Results show that 40% of refurbished brands appear in the top 25% of recommendations. We ensured the framework's robustness check, including McNemar's statistical testing on 6 subtasks (5/6 above 0.05 threshold), ablation studies with 2 alternative attention mechanisms, and validation against 14 benchmark methods, confirming framework's stability.