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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<jats:italic>YouTube<\/jats:italic>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.<\/jats:p>","DOI":"10.1017\/s0890060422000129","type":"journal-article","created":{"date-parts":[[2022,7,27]],"date-time":"2022-07-27T11:40:34Z","timestamp":1658922034000},"update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":4,"title":["Design change prediction based on social media sentiment analysis"],"prefix":"10.1017","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3441-0740","authenticated-orcid":false,"given":"Edwin C.Y.","family":"Koh","sequence":"first","affiliation":[]}],"member":"56","published-online":{"date-parts":[[2022,7,27]]},"reference":[{"key":"S0890060422000129_ref10","doi-asserted-by":"publisher","DOI":"10.1115\/1.1765117"},{"key":"S0890060422000129_ref17","doi-asserted-by":"publisher","DOI":"10.1115\/1.3149847"},{"key":"S0890060422000129_ref48","doi-asserted-by":"publisher","DOI":"10.1007\/s00163-012-0132-2"},{"key":"S0890060422000129_ref67","doi-asserted-by":"publisher","DOI":"10.1115\/1.4045687"},{"key":"S0890060422000129_ref73","doi-asserted-by":"publisher","DOI":"10.1115\/1.4044435"},{"key":"S0890060422000129_ref6","first-page":"993","article-title":"Latent Dirichlet allocation","volume":"3","author":"Blei","year":"2003","journal-title":"Journal of Machine Learning Research"},{"key":"S0890060422000129_ref22","doi-asserted-by":"publisher","DOI":"10.1007\/s00163-010-0097-y"},{"key":"S0890060422000129_ref30","doi-asserted-by":"publisher","DOI":"10.1007\/s00163-012-0131-3"},{"key":"S0890060422000129_ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.bushor.2014.11.001"},{"key":"S0890060422000129_ref46","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.112995"},{"key":"S0890060422000129_ref16","doi-asserted-by":"publisher","DOI":"10.1007\/s00163-014-0183-7"},{"key":"S0890060422000129_ref59","doi-asserted-by":"publisher","DOI":"10.1115\/1.3243634"},{"key":"S0890060422000129_ref65","unstructured":"Wickel, MC and Lindemann, U (2015) How to build up an engineering change dependency model based on past change data? 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