Determining Customer-Focused Product Features through Social Network Analysis

Xingzhi Wang; Yuchen Wang; Ang Liu

doi:10.1016/j.procir.2020.02.227

Determining Customer-Focused Product Features through Social Network Analysis

Xingzhi Wang, Yuchen Wang, Ang Liu^*

^*Corresponding author for this work

University of New South Wales

Research output: Contribution to journal › Conference article › peer-review

3 Citations (Scopus)

Abstract

Customer reviews (CR) is an emerging source to obtain customer requirements about product features. Due to customers' different preferences, identification of shared interests become important to maximize the overall satisfaction among the target group. This paper aims to provide a feature-based importance rating framework to extract, classify and analyze product features based on online CR data. The framework integrates multiple machine learning and artificial intelligence techniques, which enable CR data to be analyzed in large scale. Besides, social network analysis functions to rate the demand intensity and centrality of product features. An experimental case is conducted to showcase the applicability of the proposed framework.

Original language	English
Pages (from-to)	704-709
Number of pages	6
Journal	Procedia CIRP
Volume	91
DOIs	https://doi.org/10.1016/j.procir.2020.02.227
Publication status	Published - 2020
Externally published	Yes
Event	30th CIRP Design on Design, CIRP Design 2020 - Pretoria, South Africa Duration: 5 May 2020 → 8 May 2020

Keywords

Big Data
Customer Reviews
Data Mining
Social Network Analysis

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10.1016/j.procir.2020.02.227

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@article{d3952f1c76fc4be5b5f435a313cc0b31,

title = "Determining Customer-Focused Product Features through Social Network Analysis",

abstract = "Customer reviews (CR) is an emerging source to obtain customer requirements about product features. Due to customers' different preferences, identification of shared interests become important to maximize the overall satisfaction among the target group. This paper aims to provide a feature-based importance rating framework to extract, classify and analyze product features based on online CR data. The framework integrates multiple machine learning and artificial intelligence techniques, which enable CR data to be analyzed in large scale. Besides, social network analysis functions to rate the demand intensity and centrality of product features. An experimental case is conducted to showcase the applicability of the proposed framework.",

keywords = "Big Data, Customer Reviews, Data Mining, Social Network Analysis",

author = "Xingzhi Wang and Yuchen Wang and Ang Liu",

note = "Publisher Copyright: {\textcopyright} 2017 The Authors. Published by Elsevier B.V.; 30th CIRP Design on Design, CIRP Design 2020 ; Conference date: 05-05-2020 Through 08-05-2020",

year = "2020",

doi = "10.1016/j.procir.2020.02.227",

language = "English",

volume = "91",

pages = "704--709",

journal = "Procedia CIRP",

issn = "2212-8271",

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TY - JOUR

T1 - Determining Customer-Focused Product Features through Social Network Analysis

AU - Wang, Xingzhi

AU - Wang, Yuchen

AU - Liu, Ang

PY - 2020

Y1 - 2020

N2 - Customer reviews (CR) is an emerging source to obtain customer requirements about product features. Due to customers' different preferences, identification of shared interests become important to maximize the overall satisfaction among the target group. This paper aims to provide a feature-based importance rating framework to extract, classify and analyze product features based on online CR data. The framework integrates multiple machine learning and artificial intelligence techniques, which enable CR data to be analyzed in large scale. Besides, social network analysis functions to rate the demand intensity and centrality of product features. An experimental case is conducted to showcase the applicability of the proposed framework.

AB - Customer reviews (CR) is an emerging source to obtain customer requirements about product features. Due to customers' different preferences, identification of shared interests become important to maximize the overall satisfaction among the target group. This paper aims to provide a feature-based importance rating framework to extract, classify and analyze product features based on online CR data. The framework integrates multiple machine learning and artificial intelligence techniques, which enable CR data to be analyzed in large scale. Besides, social network analysis functions to rate the demand intensity and centrality of product features. An experimental case is conducted to showcase the applicability of the proposed framework.

KW - Big Data

KW - Customer Reviews

KW - Data Mining

KW - Social Network Analysis

UR - http://www.scopus.com/inward/record.url?scp=85091709527&partnerID=8YFLogxK

U2 - 10.1016/j.procir.2020.02.227

DO - 10.1016/j.procir.2020.02.227

M3 - Conference article

AN - SCOPUS:85091709527

SN - 2212-8271

VL - 91

SP - 704

EP - 709

JO - Procedia CIRP

JF - Procedia CIRP

T2 - 30th CIRP Design on Design, CIRP Design 2020

Y2 - 5 May 2020 through 8 May 2020

ER -

Determining Customer-Focused Product Features through Social Network Analysis

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Keywords

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