Millionaire Shoppers’ Struggles? Exploring User Experiences with Ultra-Low-Cost E-Commerce Platforms through Text Mining

Abstract

This study empirically analyzes consumer sentiment and behavior from user-generated reviews of ultra-low-cost e-commerce platforms, Temu and AliExpress. Using a Gated Recurrent Unit (GRU) model, reviews were classified as either positive or negative, while Latent Dirichlet Allocation (LDA) was employed to extract key thematic structures. A multiple regression analysis examined the influence of topics on review ratings, identifying structural links between emotions and evaluations. Results show that “delivery speed and convenience”, “value-for-money”, and “shopping convenience” drove positive sentiment. Conversely, “excessive marketing”, “system instability”, and “perceived quality issues” led to negative evaluations. Notably, the emergence of “ggang culture”—impulsive bulk buying followed by disposal—highlights a shift toward emotionally driven, irrational consumption behavior. This study contributes to quantifying the sentiment-topic-rating relationship, offering valuable implications for Chinese ultra-low-cost e-commerce expansion.