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Design change prediction based on social media sentiment analysis

Published online by Cambridge University Press:  27 July 2022

Edwin C.Y. Koh Open the ORCID record for Edwin C.Y. Koh [Opens in a new window]
Edwin C.Y. Koh*
Affiliation:
Design and Artificial Intelligence Programme & Engineering Product Development Pillar, Singapore University of Technology and Design, Singapore, Singapore
*
Author for correspondence: Edwin C.Y. Koh, E-mail: [email protected]
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Abstract

The use of artificial intelligence (AI) techniques to uncover customer sentiment is not uncommon. However, the integration of sentiment analysis with research in design change prediction remains an untapped potential. This paper presents a method that uses social media sentiment analysis to identify opportunities for design change and the set of product components affected by the change. The method builds on natural language processing to determine change candidates from textual data and uses dependency modeling to reveal direct and indirect change propagation paths arising from the change candidates. The method was applied in a case example where 3665 YouTube comments on a diesel engine were analyzed. Based on the results, two engine components were recommended for design change with six others predicted as likely to be affected through change propagation. The findings suggest that the method can be used to aid decision quality in product planning through a better understanding of the change impact associated with the opportunities identified.

Keywords

BERTdependency modelingdesign changenatural language processingsocial media
Type
Research Article
Information
AI EDAM , Volume 36 , 2022 , e24
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

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