Online User Review Analysis for Product Evaluation and Improvement

• Main Authors: Cheng Yang, Lingang Wu, Kun Tan, Chunyang Yu, Yuliang Zhou, Ye Tao, Yu Song
• Format: Article
• Language: English
• Published: MDPI AG 2021-05-01
• Series: Journal of Theoretical and Applied Electronic Commerce Research
• Subjects: online review; opinion mining; product evaluation; improvement strategy; product design; sentiment analysis
• Online Access: https://www.mdpi.com/0718-1876/16/5/90

Traditional user research methods are challenged for the decision-making in product design and improvement with the updating speed becoming faster, considering limited survey scopes, insufficient samples, and time-consuming processes. This paper proposes a novel approach to acquire useful online reviews from E-commerce platforms, build a product evaluation indicator system, and put forward improvement strategies for the product with opinion mining and sentiment analysis with online reviews. The effectiveness of the method is validated by a large number of user reviews for smartphones wherein, with the evaluation indicator system, we can accurately predict the bad review rate for the product with only 9.9% error. And improvement strategies are proposed after processing the whole approach in the case study. The approach can be applied for product evaluation and improvement, especially for the products with needs for iterative design and sailed online with plenty of user reviews.