Abstract
Mining user requirements from online review data and utilizing them as the foundation for innovative design is a key approach to improve product functionality, meet user needs, and enhance product competitiveness. This paper focuses on product requirements mining and innovative design using the online review data. Web crawler tools are used to extract and preprocess the online review data. An online review hierarchical model is established, and the LDA topic model is incorporated to extract product features and map them to various levels. Additionally, the matter-element model based on extension theory is utilized to express the online review hierarchy. The evaluation values of user opinions at various levels are calculated using the SO-PMI algorithm. For objects with lower evaluation values, defect matter-element models and user requirement affair-element models are established. By employing correlative network methods and implication system methods, complex user requirements are clarified, and effective ways to meet them are identified. Finally, a product innovation design process for online review based on the extension theory is proposed for the defect matter-element models. A software system for product requirements mining and innovative design based on online review data was designed, and the process was validated using mountain bikes as an example.
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This work was partially supported by the Guangdong Province Philosophy and Social Science Planning Project [No. GD23XGL014].
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Dong, J., Cheng, S., Yu, Z., Yang, X., Huang, M. (2024). Product Requirements Mining and Innovative Design Based on Extension Theory Using Online Review Data. In: Tan, J., Liu, Y., Huang, HZ., Yu, J., Wang, Z. (eds) Advances in Mechanical Design. ICMD 2023. Mechanisms and Machine Science, vol 155. Springer, Singapore. https://doi.org/10.1007/978-981-97-0922-9_21
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DOI: https://doi.org/10.1007/978-981-97-0922-9_21
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