TY - GEN
T1 - Improving the accuracy and diversity of feature extraction from online reviews using keyword embedding and two clustering methods
AU - Park, Seyoung
AU - Kim, Harrison M.
N1 - Publisher Copyright:
© 2020 American Society of Mechanical Engineers (ASME). All rights reserved.
PY - 2020
Y1 - 2020
N2 - In product design, it is essential to understand customers' preferences for product features. Traditional methods including the survey and interview are time-consuming and costly. As an alternative, research on utilizing online data for user analysis has been actively conducted. Although various methods have been proposed in this domain, most of them focus on the main features or usages of the product. However, from the manufacturer's perspective, sub-features are as crucial as main features or usages, because the preference for sub-features is necessary for component configuration in actual product development. As the first step to solve this problem, this paper proposes a methodology to extract and cluster sub-features by incorporating phrase embedding into the previous word embedding. Also, the presented methodology increases the accuracy and diversity of the clustering result by using X-means clustering as a noise filter and adopting spectral clustering.
AB - In product design, it is essential to understand customers' preferences for product features. Traditional methods including the survey and interview are time-consuming and costly. As an alternative, research on utilizing online data for user analysis has been actively conducted. Although various methods have been proposed in this domain, most of them focus on the main features or usages of the product. However, from the manufacturer's perspective, sub-features are as crucial as main features or usages, because the preference for sub-features is necessary for component configuration in actual product development. As the first step to solve this problem, this paper proposes a methodology to extract and cluster sub-features by incorporating phrase embedding into the previous word embedding. Also, the presented methodology increases the accuracy and diversity of the clustering result by using X-means clustering as a noise filter and adopting spectral clustering.
UR - http://www.scopus.com/inward/record.url?scp=85096338508&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85096338508&partnerID=8YFLogxK
U2 - 10.1115/DETC2020-22642
DO - 10.1115/DETC2020-22642
M3 - Conference contribution
AN - SCOPUS:85096338508
T3 - Proceedings of the ASME Design Engineering Technical Conference
BT - 46th Design Automation Conference (DAC)
PB - American Society of Mechanical Engineers (ASME)
T2 - ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2020
Y2 - 17 August 2020 through 19 August 2020
ER -