Abstract： To assist enterprises in improving their products, a two-stage user demand mining method based on online forum data is proposed. In the first stage, a convolutional neural network model is used to identify texts that contain unmet user demands. In the second stage, a Bi-directional Long Short-Term Memory Network-Conditional Random Field (BiLSTM-CRF) model is employed to extract user opinion quads (topic, object, attribute, attribute value) from the texts with unmet user demands and transform them into user demand quads (topic, object, attribute, expected attribute value). Experiments were conducted using data from a specific car series on the Autohome forum. The study shows that the mining method constructs a quad structure to represent user demands, demonstrates product features at a fine-grained level, and drills down into the details of the demand layer by layer to reduce the uncertainty of the demand and clarify the specific user needs, thereby enhancing the product ’s competitiveness. The feasibility and effectiveness of the method are also demonstrated through comparisons with other methods.
李奕潼，徐照光，党延忠. 基于网络论坛数据的未满足用户需求挖掘方法研究[J]. 管理学报, 2025, 22(1): 125-.
LI Yitong，XU Zhaoguang，DANG Yanzhong. Unmet User Needs Mining Method Based on Web Forum Data. Chinese Journal of Management, 2025, 22(1): 125-.
