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.

Discovery of Product Features for Redesign from User Implicit Feedback

“…Target features Bi et al [12] Software usage ecology, speaker, bring two into view, supper device, fingerprint System fluency, system fluency, battery, color, write, camera, stylus, notebook, keyboard, screen DPFR Software usage ecology, system fluency, CPU, multi-screen collaboration, supper device, bring two into view, notebook, huawei share, screen refresh rate, document DPFR without considering the production constraints Software usage ecology, system fluency, CPU, multi-screen collaboration, supper device, bring two into view, notebook, camera, stylus, keyboard economic constraints, environmental constraints, and corporate social responsibility constraints. ird, in the whole life cycle of product redesign, continuous learning techniques can be applied to learn and update knowledge constantly during product improvement [78,79]. Furthermore, although this study was applied to one example product, it has the potential to be applied in many domains in future work.…”

A knowledge-enabled approach for user experience-driven product improvement at the conceptual design stage

“…To deal with the limitations of product planning studies in the difficulty of identifying latent product features and the insufficient consideration of opportunity potential analysis of the identified features, Jeong et al (2019) proposed an opportunity algorithm based on topic modeling and sentiment analysis to identify product improvement opportunities from social media data. Zhu et al (2021) used an ontology-based fine-grained sentiment analysis method to obtain user preferences and transformed them into multi-levels for the automatic establishment of opportunity landscapes and house of quality table for improvement. Wang et al (2020) established a review-driven measurement model of customer preference, considering the inconsistencies between the numerical product ratings and the textual product reviews, for measuring customer preferences with regard to product features.…”

Product redesign considering the sensitivity of customer satisfaction

“…With the evaluation of customer satisfaction and product features, the to-be-improved features were identified. To advance accuracy of existing sentiment analysis methods in product redesign, Zhu et al (2021) proposed an integrated customer hearing and product redesign system, in which product redesign strategies are obtained by using online user-generated content. These product redesign methods considering customer satisfaction contribute a lot to customer loyalty and extension of product life cycle.…”