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

作　　者：Wenyi Tang Ling Tian Xu Zheng Ke Yan 

机构地区：[1]School of Computer Science and Engineering,University of Electronic Science and Technology of China,Chengdu,611731,China

出　　处：《Digital Communications and Networks》2024年第2期273-281,共9页数字通信与网络（英文版）

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).

摘　　要：Digital twinning enables manufacturers to create digital representations of physical entities,thus implementing virtual simulations for product development.Previous efforts of digital twinning neglect the decisive consumer feedback in product development stages,failing to cover the gap between physical and digital spaces.This work mines real-world consumer feedbacks through social media topics,which is significant to product development.We specifically analyze the prevalent time of a product topic,giving an insight into both consumer attention and the widely-discussed time of a product.The primary body of current studies regards the prevalent time prediction as an accompanying task or assumes the existence of a preset distribution.Therefore,these proposed solutions are either biased in focused objectives and underlying patterns or weak in the capability of generalization towards diverse topics.To this end,this work combines deep learning and survival analysis to predict the prevalent time of topics.We propose a specialized deep survival model which consists of two modules.The 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 structure.Moreover,a specific loss function different from regular survival models is proposed to achieve a more reasonable prediction.Extensive experiments on real-world datasets demonstrate that our model significantly outperforms the state-of-the-art methods.

关 键 词：Digital twinning Product development Topic analysis Social media 

分 类 号：TN91[电子电信—通信与信息系统]