Fusion of KANO theory and Attention-BiLSTM models for user demand analysis and trend prediction

With the rapid advancement of the internet, predicting user demand trends in user-generated content (UGC) on social media platforms can help businesses better understand user preferences , guiding decision-making and reform efforts. This paper explores product innovation by introducing a UGC-based user demand prediction technique. Initially, the BERTopic model is used to extract product attributes from UGC. The KANO model is then applied to categorize various user demands. An Attention-BiLSTM model, which incorporates empirical mode decomposition (EMD) and other features, is employed to forecast fluctuations and development trends in user demand preferences. The model’s performance is validated using different prediction datasets. To assess its effectiveness, the proposed hybrid model is compared to several leading deep learning algorithms . The combination of the KANO model and Attention-BiLSTM facilitates a more comprehensive analysis of sentiment and demand changes. Additionally, the limitation of existing sentiment-based trend prediction methods – unable to address long-range dependency problems – is overcome. The paper demonstrates the effectiveness of the proposed model framework using UGC data from “Auto-home”, highlighting the model’s superiority in prediction. Compared to state-of-the-art methods, this research improves online review analysis from a temporal perspective. This approach offers valuable insights for analyzing users’ product demand and predicting emotional trends related to products. • Utilized the LDA model to extract product attributes from UGC. • Applied the KANO model for classifying user demands. • Developed a hybrid Attention-BiLSTM model integrating EMD decomposition and other features.