Mingchen Li

Decomposition methods are widely used to process the nonlinear and nonstationary characteristics of tourism‐demand time series by decomposing the original data into different components with different timescales. However, the new issue is that some components after the first decomposition are still unstable and irregular, and there remains limited evidence on how to further process these unstable and irregular components effectively. Therefore, in this study, a novel secondary decomposition–ensemble approach, including empirical mode decomposition (EMD) and variational mode decomposition (VMD), is first proposed to forecast tourism demand. Correspondingly, the point and interval forecasting results show that the proposed approach generally improves forecasting performance relative to the selected benchmark models, particularly in the normal‐period and political‐unrest datasets. The COVID‐period results are more mixed, suggesting that the proposed framework remains competitive under extreme disruption but does not uniformly dominate all one‐stage decomposition alternatives across all markets and horizons.

Rapid advancements in artificial intelligence and the Internet of Things (IoT) have fueled the growth of furniture, transforming traditional home environments into intelligent living spaces. As consumer adoption accelerates, understanding user concerns and sentiment trends becomes crucial for brands to refine product offerings and enhance market competitiveness. This study systematically investigates consumer concerns and sentiment trends toward furniture products by analyzing user-generated reviews across two major e-commerce platforms: Jingdong and Taobao. Leveraging advanced text-mining methods including TF-IDF keyword extraction, hierarchical clustering, Graph of Words–Latent Dirichlet Allocation (GoW-LDA) topic modeling, and BERT-based sentiment analysis, this research identifies critical user preferences, product satisfaction factors, and platform-specific behavioral patterns. Results reveal distinct cross-platform differences; Jingdong users prioritize service quality, brand trust, and logistical efficiency, whereas Taobao users emphasize product aesthetics, material selection, and cost-effectiveness. The sentiment analysis demonstrates that Jingdong users exhibit more consistent and positive feedback, while sentiment on Taobao displays higher variability due to product-quality discrepancies and price sensitivity.