User requirement modeling and evolutionary analysis based on review data: Supporting the design upgrade of product attributes

In recent years, an increasing number of studies have focused on user requirement modeling based on online review texts. However, traditional methods often overlook the integration of user requirement models with product design frameworks, failing to effectively transform dynamically changing user requirements into a basis for product attribute upgrades. This paper proposes a user requirement modeling and evolutionary analysis method based on review data, supporting the design upgrade of product attributes. This approach differs from traditional user requirement modeling and analysis methods in two main aspects: (1) integrating the designer’s product design framework into the classification and modeling of user requirements; (2) analyzing the dynamic changes in user requirements during product upgrades and formulating new product attribute upgrade strategies. Initially, the study extracts three categories of product attributes that designers are concerned about from the review data: function (F), structure (S), and parameters (P), and establishes a correlation model between these product attributes. Subsequently, using natural language processing technology to calculate sentiment scores for product attributes and employing the Multi-Layer Perceptron (MLP) model to analyze the impact of product attribute sentiment on user satisfaction, the study constructs the FSP-Kano model, achieving classification and modeling of user requirements for these three categories of product attributes. Finally, based on the dynamic changes in user requirements within the FSP-Kano model, strategies for upgrading next-generation products are formulated. Additionally, the study illustrates the proposed method with the example of BYD’s “Qin” series of new energy vehicles. Our research demonstrates that the proposed method can accurately and comprehensively extract user requirements and develop successful product attribute improvement strategies for the next generation of products.