摘要
为了解决传统感性工学主观性过强、实时性差、数据少等问题,提出一种产品在线评论数据驱动的感性工学方法。以某电子商务平台智能手机在线评论为数据源,提出词频与评估、强度、活动3个维度相结合的TFEPA方法,并使用该方法提取在线评论中的感性词;为获得更合理的感性评价,采用面向在线评论的词聚类结合程度副词的方法计算感性评价值,再从认知心理学的角度,结合产品属性参数与用户感性意象,构建了基于BP神经网络的非线性映射模型,用于模拟用户心理评估机制。最后,评估了模型的泛化能力,验证了所提方法的可行性与有效性。
Aiming at the problems of high subjectivity,low real-time and few data in traditional Kansei engineering,a Kansei engineering driven by online reviews of product data.By taking the smartphone information of an e-commerce platform as the data source,the TF-EPA method integrated Term Frequency（TF）with Evaluation-Potency-Activity（EPA）was proposed,which could extract Kansei words.To obtain a more reasonable emotional evaluation,the sensory evaluation was calculated through word clustering and adverb-scoring of online reviews.From the perspective of cognitive psychology,a BP neural network was used to construct the nonlinear mapping model between product parameters and user Kansei images to simulate the user psychological assessment mechanism.Experiment on the smartphone case study demonstrated the generalization capability and effectiveness of the proposed model.
作者
李少波
全华凤
胡建军
吴永明
张安思
LI Shaobo1,2 , QUAN Huafeng1 , HU J ianjun1,3 , WU Yongming2 , ZHANG Ansi2(1. School of Mechanical Engineering, GuiZhou University, Guiyang 550025, China; 2. Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guiyang 550025, China~ 3. Department of Computer Science and Engineering University of South Carolina, Columbia SC 29208, US)
出处
《计算机集成制造系统》
EI
CSCD
北大核心
2018年第3期752-762,共11页
Computer Integrated Manufacturing Systems
基金
国家自然科学基金资助项目(51475097)
贵州省科技计划资助项目(黔科合JZ字[2014]2001
黔教合协同创新字[2015]02
黔科合平台人才[2016]5103)~~
关键词
产品在线评论
感性工学
词频
评估
强度
活动
BP神经网络
产品设计
online product reviews
Kansei engineering
term frequency
evaluation
potency
activity
BP neural networks
product design
