Product feature extraction from Chinese online reviews: application to product improvement

Online product reviews are valuable resources to collect customer preferences for product improvement. To retrieve consumer preferences, it is important to automatically extract product features from online reviews. However, product feature extraction from Chinese online reviews is challenging due to the particularity of the Chinese language. This research focuses on how to accurately extract and prioritize product features and how to establish product improvement strategies based on the extracted product features. First, an ensemble deep learning based model (EDLM) is proposed to extract and classify product features from Chinese online reviews. Second, conjoint analysis is conducted to calculate the corresponding weight of each product feature and a weight-based Kano model (WKM) is proposed to classify and prioritize product features. Various comparative experiments show that the EDLM model achieves impressive results in product feature extraction and outperforms existing state-of-the-art models used for Chinese online reviews. Moreover, this study can help product managers select the product features that have significant impact on enhancing customer satisfaction and improve products accordingly.

Automated summary

Online product reviews are valuable for collecting customer preferences to improve products. Extracting product features from Chinese online reviews is challenging. This research proposes an ensemble deep learning model for accurate extraction and classification of Chinese product features. Conjoint analysis and a weight-based Kano model are used to prioritize the features and establish product improvement strategies. The results show that the proposed model outperforms existing models and can help improve customer satisfaction by selecting significant product features.