Quantification of Design Parameters through Analysis of User-Generated Contents

Nam Youl Kim; Jong Hyeong Kim; Sunghae Kim

doi:10.3795/KSME-A.2022.46.3.277

Quantification of Design Parameters through Analysis of User-Generated Contents

Nam Youl Kim
, Jong Hyeong Kim
, Sunghae Kim

Dept. of Mechanical System and Design Engineering

Seoul National University of Science and Technology (SNUST)
GKeS

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

The growth of Web 2.0 has facilitated the sharing of opinions and experiences gained from products and services. Furthermore, the development of artificial-intelligence technology has enabled the extraction of various information from these user-generated content (UGC) data. This paper reports on the extraction of useful design parameters by processing UGC data written in natural language. The information collection method and quantification model of the target product to be analyzed are presented, and the natural-language-processing process is described. Although such quantifications have not been actively performed, they can help reduce the complexity of design and learning for novice designers. The necessity and significance of this method are illustrated using cases from outside Korea.

Original language	English
Pages (from-to)	277-286
Number of pages	10
Journal	Transactions of the Korean Society of Mechanical Engineers, A
Volume	66
Issue number	3
DOIs	https://doi.org/10.3795/KSME-A.2022.46.3.277
State	Published - 2022

Keywords

Design Automation
Design Parameter Quantification
Extract Design Parameter
Natural Language Processing
Preference based Design
Quantification Model
User Generated Contents

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10.3795/KSME-A.2022.46.3.277

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title = "Quantification of Design Parameters through Analysis of User-Generated Contents",

abstract = "The growth of Web 2.0 has facilitated the sharing of opinions and experiences gained from products and services. Furthermore, the development of artificial-intelligence technology has enabled the extraction of various information from these user-generated content (UGC) data. This paper reports on the extraction of useful design parameters by processing UGC data written in natural language. The information collection method and quantification model of the target product to be analyzed are presented, and the natural-language-processing process is described. Although such quantifications have not been actively performed, they can help reduce the complexity of design and learning for novice designers. The necessity and significance of this method are illustrated using cases from outside Korea.",

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AU - Kim, Nam Youl

AU - Kim, Jong Hyeong

AU - Kim, Sunghae

PY - 2022

Y1 - 2022

N2 - The growth of Web 2.0 has facilitated the sharing of opinions and experiences gained from products and services. Furthermore, the development of artificial-intelligence technology has enabled the extraction of various information from these user-generated content (UGC) data. This paper reports on the extraction of useful design parameters by processing UGC data written in natural language. The information collection method and quantification model of the target product to be analyzed are presented, and the natural-language-processing process is described. Although such quantifications have not been actively performed, they can help reduce the complexity of design and learning for novice designers. The necessity and significance of this method are illustrated using cases from outside Korea.

AB - The growth of Web 2.0 has facilitated the sharing of opinions and experiences gained from products and services. Furthermore, the development of artificial-intelligence technology has enabled the extraction of various information from these user-generated content (UGC) data. This paper reports on the extraction of useful design parameters by processing UGC data written in natural language. The information collection method and quantification model of the target product to be analyzed are presented, and the natural-language-processing process is described. Although such quantifications have not been actively performed, they can help reduce the complexity of design and learning for novice designers. The necessity and significance of this method are illustrated using cases from outside Korea.

KW - Design Automation

KW - Design Parameter Quantification

KW - Extract Design Parameter

KW - Natural Language Processing

KW - Preference based Design

KW - Quantification Model

KW - User Generated Contents

UR - https://www.scopus.com/pages/publications/85127146390

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DO - 10.3795/KSME-A.2022.46.3.277

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VL - 66

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EP - 286

JO - Transactions of the Korean Society of Mechanical Engineers, A

JF - Transactions of the Korean Society of Mechanical Engineers, A

IS - 3

ER -

Quantification of Design Parameters through Analysis of User-Generated Contents

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Keywords

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