User needs insights from UGC based on large language model

With limited resources, it is critical for companies to understand and address user needs to gain a competitive edge.The methods that utilize large-scale user-generated content (UGC) produced by the internet can analyze user needs efficiently and accurately. However, these methods have not been extensively studied.This paper proposes a framework based on large language model (LLM) to extract user's insights into the priority of product attributes. First, product attributes are extracted from user reviews using LLM. Then, the mapping network between user reviews and satisfaction is established through sentiment analysis based on the LLM and Multi-layer Perceptron (MLP) classification. Finally, a comprehensive analysis of product importance is conducted using a proposed quantified IPA-Kano model. An empirical study on smart wearable bands is conducted to offer an intuitive and quantifiable analysis of user attention and satisfaction for each product attribute. The strengths and weaknesses of the products are highlighted, providing valuable insights that can inspire companies to adopt user-centric optimization strategies.