Mining novel customer needs for product design from user-generated content through large language model-enabled data augmentation and ensemble learning

Detecting novel customer needs from user-generated content such as online product reviews is essential for supporting product innovation and improving customer satisfaction. However, challenges arise due to severe class imbalance, where only a small portion of the content reflects truly novel needs, and the presence of noisy or irrelevant information. In response, this paper presents a new framework that combines generative artificial intelligence with ensemble learning. We use LLaMA, a powerful open-source large language model, to generate diverse meaningful positive samples that reduce data sparsity. With this augmented dataset, we train multiple BERT-based classifiers, each on different subsets of the data, and integrate their predictions using a hard voting strategy to improve accuracy and robustness. Experiments on a dataset of Amazon laptop reviews show that the proposed method achieves superior performance in recall and F1 score compared with standard baselines. This performance capability helps ensure that valuable insights are not missed. We offer a practical approach for applying generative models to support product development, laying the foundation for future studies in artificial intelligence for business innovation.