Analyzing topics in social media for improving digital twinning based product development

作     者：Wenyi Tang Ling Tian Xu Zheng Ke Yan Wenyi Tang;Ling Tian;Xu Zheng;Ke Yan

作者机构：School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu611731China 

出 版 物：《Digital Communications and Networks》 (数字通信与网络（英文版）)

年 卷 期：2024年第10卷第2期

页      面：273-281页

核心收录：

学科分类：0810[工学-信息与通信工程] 08[工学] 081001[工学-通信与信息系统] 

基　　金：supported by Sichuan Science and Technology Program(Nos.2019YFG0507,2020YFG0328 and 2021YFG0018) by National Natural Science Foundation of China(NSFC)under Grant No.U19A2059 by the Young Scientists Fund of the National Natural Science Foundation of China under Grant No.61802050 by the Fundamental Research Funds for the Central Universities(No.ZYGX2021J019) 

主　　题：Digital twinning Product development Topic analysis Social media 

摘      要：Digital twinning enables manufacturers to create digital representations of physical entities,thus implementing virtual simulations for product *** efforts of digital twinning neglect the decisive consumer feedback in product development stages,failing to cover the gap between physical and digital *** work mines real-world consumer feedbacks through social media topics,which is significant to product *** specifically analyze the prevalent time of a product topic,giving an insight into both consumer attention and the widely-discussed time of a *** primary body of current studies regards the prevalent time prediction as an accompanying task or assumes the existence of a preset ***,these proposed solutions are either biased in focused objectives and underlying patterns or weak in the capability of generalization towards diverse *** this end,this work combines deep learning and survival analysis to predict the prevalent time of *** propose a specialized deep survival model which consists of two *** first module enriches input covariates by incorporating latent features of the time-varying text,and the second module fully captures the temporal pattern of a rumor by a recurrent network ***,a specific loss function different from regular survival models is proposed to achieve a more reasonable *** experiments on real-world datasets demonstrate that our model significantly outperforms the state-of-the-art methods.