Multi-modal online review driven product improvement design based on scientific effects knowledge graph

Abstract

Online reviews serve as significant channels for users to express their preferences, constituting an essential data source for enterprises to identify product requirements. However, with the widespread adoption of smartphones, the act of capturing spontaneous photographs has become a habitual practice for the majority, resulting in the increasing prevalence of supplementary visual expressions within online reviews. Therefore, an important research question emerges: How can product requirements be effectively extracted from multimodal online reviews and subsequently translated into product design proposals? In this paper, we establish a framework, seamlessly integrating aspect-based sentiment analysis, product requirement identification, and requirement mapping based on a scientific effect knowledge graph. Firstly, we conduct aspect term extraction on the online reviews, followed by aspect sentiment classification. Subsequently, we delve deeper into the analyzed results obtained from aspect-based sentiment analysis to identify preferences in product requirements. Finally, we employ requirement mapping based on a scientific effect knowledge graph to generate proposals for product design improvements. To validate the efficacy of our approach, we conducted experiments and the results demonstrate that our method outperforms alternative approaches, while the requirement mapping based on a scientific effect knowledge graph efficiently facilitates the realisation of product design improvements.

KEYWORDS:

• Product improvement
• knowledge graph
• requirement identification
• sentiment analysis

Acknowledgments

The authors are also grateful to the anonymous reviewers for their valuable suggestions for improving the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

All the research data presented in the work can be reached when contacting the author by email with a reasonable request.

Additional information

Funding

This research is supported by the National Key R&D Program of China under Grant 2020YFB1711401.