基于混合神经网络的中文在线评论产品特征提取及消费者需求分析*

[Objective] This study aims to extract product features and analyze customer needs based on the content of Chinese online reviews. [Methods] First, we proposed a hybrid neural network (HNN) to extract product features. Then, we applied critical incident technique (CIT) and analysis of complaints and compliments (ACC) to the Kano model to classify and prioritize product features. [Results] The F1 value of the HNN model reached 94.85%, which was 10.52 percentage points higher than the variant benchmark models and 9.47 percentage points over other leading models on average. [Limitations] The proposed model is supervised learning, and the need for labeling information restricts its application. [Conclusions] The proposed method improves the accuracy of product feature extraction, as well as classifies and prioritizes product features based on customer needs. It lays a foundation for managers to develop product improvement strategies.