Intelligent product redesign strategy with ontology-based fine-grained sentiment analysis

Abstract With the increasing demand for a personalized product and rapid market response, many companies expect to explore online user-generated content (UGC) for intelligent customer hearing and product redesign strategy. UGC has the advantages of being more unbiased than traditional interviews, yielding in-time response, and widely accessible with a sheer volume. From online resources, customers’ preferences toward various aspects of the product can be exploited by promising sentiment analysis methods. However, due to the complexity of language, state-of-the-art sentiment analysis methods are still not accurate for practice use in product redesign. To tackle this problem, we propose an integrated customer hearing and product redesign system, which combines the robust use of sentiment analysis for customer hearing and coordinated redesign mechanisms. Ontology and expert knowledges are involved to promote the accuracy. Specifically, a fuzzy product ontology that contains domain knowledges is first learned in a semi-supervised way. Then, UGC is exploited with a novel ontology-based fine-grained sentiment analysis approach. Extracted customer preference statistics are transformed into multilevels, for the automatic establishment of opportunity landscapes and house of quality table. Besides, customer preference statistics are interactively visualized, through which representative customer feedbacks are concurrently generated. Through a case study of smartphone, the effectiveness of the proposed system is validated, and applicable redesign strategies for a case product are provided. With this system, information including customer preferences, user experiences, using habits and conditions can be exploited together for reliable product redesign strategy elicitation.