A Customer Demand Mining Algorithm Based on Online Comments and Machine Learning - Yang, Yong; Li, Qiaoxing (International Journal of Industrial Engineering: Theory, Applications and Practice, 2025)
In the current market environment, the phenomenon of product homogenization is severe. If enterprises cannot deeply understand customer needs and provide differentiated products or services, it is difficult to stand out in the competition. In order to effectively improve overall customer satisfaction and enhance the market competitiveness of enterprises, a customer demand mining algorithm based on online comments and machine learning is proposed. Collect customer demand information data through online comments and process the collected data with redundant information to improve the efficiency and accuracy of demand mining. On this basis, customer demand attribute features have been further extracted, and a customer demand clustering mining model has been constructed using a self-organizing mapping neural network. By training the model, the final clustering mining results can be obtained, thus achieving precise mining of customer needs. This study clearly addresses a key issue in the current field of consumer demand mining: how to efficiently and accurately identify and utilize consumer demand information in online comments. By constructing a clustering mining model based on the Self-Organizing Maps (SOM) neural network, this study fills the literature gap in this field and provides more accurate and practical consumer demand analysis methods for enterprises. The experimental results show that, compared with the three comparison methods, the proposed method has a 98% feasibility of customer demand mining and 92% customer satisfaction. It shows that the proposed method has high feasibility and customer satisfaction for customer demand mining and has a better overall customer demand mining effect. This provides strong support for improving overall customer satisfaction and corporate competitiveness.