In this paper, based on more than ten years' studies on this dedicated research thrust, a comprehensive review concerning information mining from big consumer opinion data in order to assist product design is presented. First, the research background and the essential terminologies regarding online consumer opinion data are introduced. Next, studies concerning information extraction and information utilization of big consumer opinion data for product design are reviewed. Studies on information extraction of big consumer opinion data are explained from various perspectives, including data acquisition, opinion target recognition, feature identification and sentiment analysis, opinion summarization and sampling, etc. Reviews on information utilization of big consumer opinion data for product design are explored in terms of how to extract critical customer needs from big consumer opinion data, how to connect the voice of the customers with product design, how to make effective comparisons and reasonable ranking on similar products, how to identify ever-evolving customer concerns efficiently, and so on. Furthermore, significant and practical aspects of research trends are highlighted for future studies. This survey will facilitate researchers and practitioners to understand the latest development of relevant studies and applications centered on how big consumer opinion data can be processed, analyzed, and exploited in aiding product design.

References

References
1.
Otterbacher
,
J.
,
2009
, “
‘Helpfulness’ in Online Communities: A Measure of Message Quality
,”
SIGCHI Conference on Human Factors in Computing Systems
(
CHI'09
), Boston, MA, Apr. 4–9, pp.
955
964
https://dl.acm.org/citation.cfm?id=1518848.
2.
Kim
,
S.-M.
,
Pantel
,
P.
,
Chklovski
,
T.
, and
Pennacchiotti
,
M.
,
2006
, “
Automatically Assessing Review Helpfulness
,”
Conference on Empirical Methods in Natural Language Processing
(
EMNLP
), Sydney, Australia, July 22–23, pp.
423
430
https://dl.acm.org/citation.cfm?id=1610135.
3.
Ghose
,
A.
, and
Ipeirotis
,
P. G.
,
2007
, “
Designing Novel Review Ranking Systems: Predicting the Usefulness and Impact of Reviews
,”
Ninth International Conference on Electronic Commerce
(
ICEC
), Minneapolis, MN, Aug. 19–22, pp.
303
10
https://dl.acm.org/citation.cfm?id=1282158.
4.
Liu
,
Y.
,
Huang
,
X.
,
An
,
A.
, and
Yu
,
X.
,
2008
, “
HelpMeter: A Nonlinear Model for Predicting the Helpfulness of Online Reviews
,”
IEEE/WIC/ACM
International Conference on Web Intelligence and Intelligent Agent Technology
, Sydney, Australia, Dec. 9–12, pp.
793
796
.
5.
Liu
,
Y.
,
Huang
,
X.
,
An
,
A.
, and
Yu
,
X.
,
2008b
, “
Modeling and Predicting the Helpfulness of Online Reviews
,”
Eighth IEEE International Conference on Data Mining
, Pisa, Italy, Dec. 15–19, pp.
443
52
.
6.
Zhang
,
R.
, and
Tran
,
T.
,
2008
, “
An Entropy-Based Model for Discovering the Usefulness of Online Product Reviews
,”
IEEE/WIC/ACM
International Conference on Web Intelligence and Intelligent Agent Technology
, Sydney, Australia, Dec. 9–12, pp.
759
–7
62
.
7.
Danescu-Niculescu-Mizil
,
C.
,
Kossinets
,
G.
,
Kleinberg
,
J.
, and
Lee
,
L.
,
2009
, “
How Opinions are Received by Online Communities: A Case Study on Amazon.com Helpfulness Votes
,”
18th International Conference on World Wide Web
(WWW)
, Madrid, Spain, Apr. 20–24, pp.
141
150
https://dl.acm.org/citation.cfm?id=1526729.
8.
Miao
,
Q.
,
Li
,
Q.
, and
Dai
,
R.
,
2009
, “
AMAZING: A Sentiment Mining and Retrieval System
,”
Expert Syst. Appl.
,
36
(
3
), pp.
7192
7198
.
9.
O'Mahony
,
M. P.
, and
Smyth
,
B.
,
2009
, “
Learning to Recommend Helpful Hotel Reviews
,”
Third ACM Conference on Recommender Systems (RecSys'09)
, New York, Oct. 23–25, pp.
305
308
.
10.
Zhang
,
R.
, and
Tran
,
T.
,
2011
, “
An Information Gain-Based Approach for Recommending Useful Product Reviews
,”
Knowl. Inf. Syst.
,
26
(
3
), pp.
419
434
.
11.
Hong
,
Y.
,
Lu
,
J.
,
Yao
,
J.
,
Zhu
,
Q.
, and
Zhou
,
G.
,
2012
, “
What Reviews are Satisfactory: Novel Features for Automatic Helpfulness Voting
,”
35th International ACM SIGIR Conference on Research and Development in Information Retrieval
(
SIGIR
), Portland, OR, Aug. 12–16, pp.
495
504
https://dl.acm.org/citation.cfm?id=2348351.
12.
Zheng
,
X.
,
Zhu
,
S.
, and
Lin
,
Z.
,
2013
, “
Capturing the Essence of Word-of-Mouth for Social Commerce: Assessing the Quality of Online E-Commerce Reviews by a Semi-Supervised Approach
,”
Decis. Support Syst.
,
56
, pp.
211
222
.
13.
Yu
,
X.
,
Liu
,
Y.
,
Huang
,
X.
, and
An
,
A.
,
2010
, “
A Quality-Aware Model for Sales Prediction Using Reviews
,”
19th International Conference on World Wide Web
(
WWW'10
), Raleigh, NC, Apr. 26–30, pp.
1217
1218
https://dl.acm.org/citation.cfm?id=1772882.
14.
Ngo-Ye
,
T. L.
, and
Sinha
,
A. P.
,
2012
, “
Analyzing Online Review Helpfulness Using a Regressional ReliefF-Enhanced Text Mining Method
,”
ACM Trans. Manage. Inf. Syst.
,
3
(
2
), pp.
1
20
.
15.
Liu
,
J.
,
Cao
,
Y.
,
Lin
,
C.-Y.
,
Huang
,
Y.
, and
Zhou
,
M.
,
2007
, “
Low-Quality Product Review Detection in Opinion Summarization
,” Conference on Empirical Methods in Natural Language Processing and on Computational Natural Language Learning (
EMNLP
), Prague, Czech Republic, pp.
334
342
.
16.
Zhang
,
R.
, and
Tran
,
T.
,
2010
, “
A Novel Approach for Recommending Ranked User-Generated Reviews
,”
Advances in Artificial Intelligence
, Vol. 6085, Springer, Berlin, pp.
324
327
.
17.
Chen
,
C. C.
, and
Tseng
,
Y.-D.
,
2011
, “
Quality Evaluation of Product Reviews Using an Information Quality Framework
,”
Decis. Support Syst.
,
50
(
4
), pp.
755
768
.
18.
Li
,
Y.
,
Ye
,
Q.
,
Zhang
,
Z.
, and
Wang
,
T.
,
2011
, “
Snippet-Based Unsupervised Approach for Sentiment Classification of Chinese Online Reviews
,”
Int. J. Inf. Technol. Decis. Making
,
10
(
6
), pp.
1097
1110
.
19.
Ying
,
L.
,
Jin
,
J.
,
Ji
,
P.
,
Harding
,
J. A.
, and
Fung
,
R. Y. K.
,
2013
, “
Identifying Helpful Online Reviews: A Product Designer's Perspective
,”
Comput.-Aided Des.
,
45
(
2
), pp.
180
194
.
20.
Jindal
,
N.
, and
Liu
,
B.
,
2008
, “
Opinion Spam and Analysis
,”
International Conference on Web Search and Data Mining (WSDM)
, Palo Alto, CA, Feb. 11–12, pp. 219–230https://dl.acm.org/citation.cfm?id=1341560.
21.
Jindal
,
N.
,
Liu
,
B.
, and
Lim
,
E.-P.
,
2010
, “
Finding Unusual Review Patterns Using Unexpected Rules
,”
19th ACM International Conference on Information and Knowledge Management
(
CIKM'10
), Toronto, ON, Canada, Oct. 26–30, pp.
1549
1552
https://dl.acm.org/citation.cfm?id=1871669.
22.
Wu
,
G.
,
Greene
,
D.
, and
Cunningham
,
P.
,
2010
, “
Merging Multiple Criteria to Identify Suspicious Reviews
,”
Fourth ACM Conference on Recommender Systems
(
RecSyS'10
), Barcelona, Spain, Sept. 26–30, pp.
241
44
https://dl.acm.org/citation.cfm?id=1864708.1864757.
23.
Ott
,
M.
,
Choi
,
Y.
,
Cardie
,
C.
, and
Jeffrey
,
T. H.
,
2011
, “
Finding Deceptive Opinion Spam by Any Stretch of the Imagination
,”
49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies
(
HLT
), Portland, OR, June 19–24, pp.
309
19
https://dl.acm.org/citation.cfm?id=2002512.
24.
Ott
,
M.
,
Cardie
,
C.
, and
Hancock
,
J.
,
2012
, “
Estimating the Prevalence of Deception in Online Review Communities
,”
21st International Conference on World Wide Web
(
WWW'12
), Lyon, France, Apr. 16–20, pp.
201
10
https://dl.acm.org/citation.cfm?id=2187864.
25.
Lau
,
R. Y. K.
,
Liao
,
S. Y.
,
Kwok
,
R. C.-W.
,
Xu
,
K.
,
Xia
,
Y.
, and
Li
,
Y.
,
2012
, “
Text Mining and Probabilistic Language Modeling for Online Review Spam Detection
,”
ACM Trans. Manage. Inf. Syst.
,
2
(
4
), p.
25
.
26.
Morales
,
A.
,
Sun
,
H.
, and
Yan
,
X.
,
2013
, “
Synthetic Review Spamming and Defense
,” WWW'13 Companion, Rio de Janeiro, Brazil, pp.
155
56
.
27.
Song
,
L.
,
Lau
,
R. Y. K.
,
Kwok
,
R. C.-W.
,
Mirkovski
,
K.
, and
Dou
,
W.
,
2017
, “
Who are the Spoilers in Social Media Marketing? Incremental Learning of Latent Semantics for Social Spam Detection
,”
Electron. Commerce Res.
,
17
(
1
), pp.
51
81
.
28.
Xie
,
S.
,
Wang
,
G.
,
Lin
,
S.
, and
Philip
,
S. Y.
,
2012
, “
Review Spam Detection Via Time Series Pattern Discovery
,”
WWW'12 Companion
, Lyon, France, pp.
635
636
.
29.
Xie
,
S.
,
Wang
,
G.
,
Lin
,
S.
, and
Philip
,
S. Y.
,
2012
, “
Review Spam Detection Via Temporal Pattern Discovery
,”
18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
(
KDD'12
), Beijing, China, Aug. 12–16, pp.
823
831
https://dl.acm.org/citation.cfm?id=2339662.
30.
Lim
,
E.-P.
,
Nguyen
,
V.-A.
,
Jindal
,
N.
,
Liu
,
B.
, and
Lauw
,
H. W.
,
2010
, “
Detecting Product Review Spammers Using Rating Behaviors
,”
19th ACM International Conference on Information and Knowledge Management
(
CIKM'10
), Toronto, ON, Canada, Oct. 26–30, pp.
939
948
https://dl.acm.org/citation.cfm?id=1871557.
31.
Wang
,
G.
,
Xie
,
S.
,
Liu
,
B.
, and
Yu
,
P. S.
,
2012
, “
Identify Online Store Review Spammers Via Social Review Graph
,”
ACM Trans. Intell. Syst. Technol.
,
3
(
4
), pp.
61:1
61:21
.
32.
Kokkodis
,
M.
,
2012
, “
Learning From Positive and Unlabeled Amazon Reviews: Towards Identifying Trustworthy Reviewers
,”
21st International Conference on World Wide Web
(
WWW'12 Companion
), Lyon, France, Apr. 16–20, pp.
545
546
https://dl.acm.org/citation.cfm?id=2188119&dl=ACM&coll=DL.
33.
Mukherjee
,
A.
,
Liu
,
B.
,
Wang
,
J.
,
Glance
,
N.
, and
Jindal
,
N.
,
2011
, “
Detecting Group Review Spam
,” 20th International Conference Companion on World Wide Web (
WWW'11
), Hyderabad, India, Mar. 28–Apr. 1, pp.
93
94
https://dl.acm.org/citation.cfm?id=1963192.1963240.
34.
Mukherjee
,
A.
,
Liu
,
B.
, and
Glance
,
N.
,
2012
, “
Spotting Fake Reviewer Groups in Consumer Reviews
,” 21st international conference on World Wide Web (
WWW'12
), Lyon, France, Apr. 16–20, pp.
191
200
https://dl.acm.org/citation.cfm?id=2187863.
35.
Dalvi
,
N.
,
Kumar
,
R.
,
Pang
,
B.
, and
Tomkins
,
A.
,
2009
, “
A Translation Model for Matching Reviews to Objects
,”
CIKM'09
, Hong Kong, China, pp.
167
176
.
36.
Liu
,
K.
,
Xu
,
L.
, and
Zhao
,
J.
,
2012
, “
Opinion Target Extraction Using Word-Based Translation Model
,”
Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
(
EMNLP-CoNLL '12
), Jeju Island, South Korea, July 12–14, pp.
1346
1356
https://dl.acm.org/citation.cfm?id=2391101.
37.
Zhang
,
Q.
,
Wu
,
Y.
,
Wu
,
Y.
, and
Huang
,
X.
,
2011
, “
Opinion Mining With Sentiment Graph
,”
IEEE/WIC/ACM
International Conferences on Web Intelligence and Intelligent Agent Technology, Lyon, France, Aug. 22–27, pp.
249
52
.
38.
Ma
,
T.
, and
Wan
,
X.
,
2010
, “
Opinion Target Extraction in Chinese News Comments
,”
23rd International Conference on Computational Linguistics: Posters
(
COLING'10
), Beijing, China, Aug. 23–27, pp.
782
90
https://dl.acm.org/citation.cfm?id=1944656.
39.
Yao
,
Y.
, and
Sun
,
A.
,
2014
, “
Product Name Recognition and Normalization in Internet Forums
,”
SIGIR'14
, Gold Coast, Australia, July 6–11.
40.
Yao
,
Y.
, and
Sun
,
A.
,
2015
, “
Mobile Phone Name Extraction From Internet Forums: A Semi-Supervised Approach
,”
World Wide Web
, 19(5), pp. 783–805.
41.
Jakob
,
N.
, and
Gurevych
,
I.
,
2010
, “
Extracting Opinion Targets in a Single- and Cross-Domain Setting With Conditional Random Fields
,”
Conference on Empirical Methods in Natural Language Processing
(
EMNLP'10
), Cambridge, MA, Oct. 9–11, pp.
1035
45
https://dl.acm.org/citation.cfm?id=1870759.
42.
Ding
,
X.
, and
Liu
,
B.
,
2007
, “
The Utility of Linguistic Rules in Opinion Mining
,”
30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
(
SIGIR'07
), Amsterdam, The Netherlands, July 23–27, pp. 811–812.https://dl.acm.org/citation.cfm?id=1277921
43.
Ding
,
X.
,
Liu
,
B.
, and
Philip S
,
Y.
,
2008
, “
A Holistic Lexicon-Based Approach to Opinion Mining
,”
International Conference on Web Search and Data Mining
(
WSDM'08
), Palo Alto, CA, Feb. 11–12, pp. 231–240https://dl.acm.org/citation.cfm?id=1341561.
44.
Kobayakawa
,
T. S.
,
Kumano
,
T.
,
Tanaka
,
H.
,
Okazaki
,
N.
,
Kim
,
J.-D.
, and
Tsujii
,
J.
,
2009
, “
Opinion Classification With Tree Kernel SVM Using Linguistic Modality Analysis
,”
8th ACM Conference on Information and Knowledge Management
(
CIKM'09
), Hong Kong, China, Nov. 2–6, pp.
1791
1794
https://dl.acm.org/citation.cfm?doid=1645953.1646231.
45.
Polpinij
,
J.
, and
Ghose
,
A. K.
,
2008
, “
An Ontology-Based Sentiment Classification Methodology for Online Consumer Reviews
,”
IEEE/WIC/ACM
International Conference on Web Intelligence and Intelligent Agent Technology, Sydney, Australia, Dec. 9–12
, pp.
518
524
.
46.
Hassan
,
A.
, and
Radev
,
D.
,
2010
, “
Identifying Text Polarity Using Random Walks
,”
48th Annual Meeting of the Association for Computational Linguistics
(
ACL'10
), Uppsala, Sweden, July 11–16, pp.
395
403
.https://dl.acm.org/citation.cfm?id=1858722
47.
Qiu
,
G.
,
Liu
,
B.
,
Bu
,
J.
, and
Chen
,
C.
,
2009
, “
Expanding Domain Sentiment Lexicon Through Double Propagation
,”
21st International Joint Conference on Artificial Intelligence
(
IJCAI'09
), Pasadena, CA, July 11–17, pp.
1199
1204
.https://dl.acm.org/citation.cfm?id=1661637
48.
Hassan
,
A.
,
Qazvinian
,
V.
, and
Radev
,
D.
,
2010
, “
What's With the Attitude?: Identifying Sentences With Attitude in Online Discussions
,”
Conference on Empirical Methods in Natural Language Processing
(
EMNLP'10
), Cambridge, MA, Oct. 9–11, pp.
1245
55
https://dl.acm.org/citation.cfm?id=1870779.
49.
Mei
,
Q.
,
Ling
,
X.
,
Wondra
,
M.
,
Su
,
H.
, and
Zhai
,
C. X.
,
2007
, “
Topic Sentiment Mixture: Modeling Facets and Opinions in Weblogs
,” 16th International Conference on World Wide Web
(WWW'07)
, Banff, AB, Canada, May 8–12, pp.
171
180
https://dl.acm.org/citation.cfm?id=1242596.
50.
Zhao
,
W. X.
,
Jiang
,
J.
,
Yan
,
H.
, and
Li
,
X.
,
2010
, “
Jointly Modeling Aspects and Opinions With a MaxEnt-LDA Hybrid
,”
Conference on Empirical Methods in Natural Language Processing
(
EMNLP'10
), Cambridge, MA, Oct. 9–11, July 27–31, pp.
56
65
https://dl.acm.org/citation.cfm?id=1870664.
51.
Wu
,
Y.
,
Zhang
,
Q.
,
Huang
,
X.
, and
Wu
,
L.
,
2011
, “
Structural Opinion Mining for Graph-Based Sentiment Representation
,”
Conference on Empirical Methods in Natural Language Processing
(
EMNLP'11
), Edinburgh, UK, July 27–31, pp.
1332
1341
https://dl.acm.org/citation.cfm?id=2145572.
52.
Bespalov
,
D.
,
Bai
,
B.
,
Qi
,
Y.
, and
Shokoufandeh
,
A.
,
2011
, “
Sentiment Classification Based on Supervised Latent N-Gram Analysis
,”
20th ACM International Conference on Information and Knowledge Management
(
CIKM'11
), Glasgow, UK, Oct. 24–28, pp. 375–382.https://dl.acm.org/citation.cfm?id=2063635
53.
Liu
,
T.
,
Li
,
M.
,
Zhou
,
S.
, and
Du
,
X.
,
2011
, “
Sentiment Classification Via L2-Norm Deep Belief Network
,”
20th ACM International Conference on Information and Knowledge Management
(
CIKM'11
), Glasgow, UK, Oct. 24–28, pp.
2489
2492
https://dl.acm.org/citation.cfm?id=2063999.
54.
Long
,
G.
,
Chen
,
L.
,
Zhu
,
X.
, and
Zhang
,
C.
,
2012
, “
TCSST: Transfer Classification of Short & Sparse Text Using External Data
,”
21st ACM International Conference on Information and Knowledge Management
(
CIKM'12
), Maui, HI, Oct. 29–Nov. 2, pp.
764
772
https://dl.acm.org/citation.cfm?id=2396859.
55.
Hu
,
X.
, and
Wu
,
B.
,
2009
, “
Classification and Summarization of Pros and Cons for Customer Reviews
,”
IEEE/WIC/ACM
International Joint Conference on Web Intelligence and Intelligent Agent Technology, Milan, Italy, Sept. 15–18
, pp.
73
76
.
56.
Zagibalov
,
T.
, and
Carroll
,
J.
,
2008
, “
Automatic Seed Word Selection for Unsupervised Sentiment Classification of Chinese Text
,” 22nd International Conference on Computational Linguistics (
COLING'08
), Manchester, UK, Aug. 18–22, pp.
1073
1080
https://dl.acm.org/citation.cfm?id=1599216.
57.
Lau
,
R. Y. K.
,
Lai
,
C. L.
,
Peter
,
B. B.
, and
Kam
,
F. W.
,
2011
, “
Leveraging Web 2.0 Data for Scalable Semi-Supervised Learning of Domain-Specific Sentiment Lexicons
,”
20th ACM International Conference on Information and Knowledge Management
(
CIKM'11
), Glasgow, UK, pp. Oct. 24–28,
2457
2460
https://dl.acm.org/citation.cfm?id=2063991.
58.
Lin
,
K. H.-Y.
,
Yang
,
C.
, and
Chen
,
H.-H.
,
2008
, “
Emotion Classification of Online News Articles From the Reader's Perspective
,”
IEEE/WIC/ACM
International Conference on Web Intelligence and Intelligent Agent Technology, Sydney, Australia, Dec. 9–12
, pp.
220
226
.
59.
Sindhwani
,
V.
, and
Melville
,
P.
,
2008
, “
Document-Word Co-Regularization for Semi-Supervised Sentiment Analysis
,” Eighth IEEE International Conference on Data Mining (
ICDM'08
), Pisa, Italy, Dec. 15–18, pp.
1025
1030
.
60.
Hu
,
X.
,
Tang
,
L.
,
Tang
,
J.
, and
Liu
,
H.
,
2013
, “
Exploiting Social Relations for Sentiment Analysis in Microblogging
,”
Sixth ACM International Conference on Web Search and Data Mining
(
WSDM'13
), Rome, Italy, Feb. 4–8, pp.
537
546
https://dl.acm.org/citation.cfm?id=2433465.
61.
Liu
,
J.
, and
Seneff
,
S.
,
2009
, “
Review Sentiment Scoring Via a Parse-and-Paraphrase Paradigm
,”
Conference on Empirical Methods in Natural Language Processing
(
EMNLP'09
), Singapore, Aug. 6–7, pp.
161
69
https://dl.acm.org/citation.cfm?id=1699532.
62.
Wu
,
Y.
,
Zhang
,
Q.
,
Huang
,
X.
, and
Wu
,
L.
,
2009
, “
Phrase Dependency Parsing for Opinion Mining
,”
Conference on Empirical Methods in Natural Language Processing
(
EMNLP'09
), Singapore, Aug. 6–7, pp.
1533
1541
https://dl.acm.org/citation.cfm?id=1699700.
63.
Zhang
,
Q.
,
Wu
,
Y.
,
Li
,
T.
,
Ogihara
,
M.
,
Johnson
,
J.
, and
Huang
,
X.
,
2009
, “
Mining Product Reviews Based on Shallow Dependency Parsing
,”
32nd International ACM SIGIR Conference on Research and Development in Information Retrieval
(
SIGIR'09
), Boston, MA, July 19–23, pp.
726
727
https://dl.acm.org/citation.cfm?id=1572098.
64.
Wei
,
W.
, and
Gulla
,
J. A.
,
2010
, “
Sentiment Learning on Product Reviews Via Sentiment Ontology Tree
,”
48th Annual Meeting of the Association for Computational Linguistics
(
ACL'10
), Uppsala, Sweden, July 11–16, pp.
404
413
https://dl.acm.org/citation.cfm?id=1858723.
65.
Cataldi
,
M.
,
Ballatore
,
A.
,
Tiddi
,
I.
, and
Aufaure
,
M.-A.
,
2013
, “
Good Location, Terrible Food: Detecting Feature Sentiment in User-Generated Reviews
,”
Social Network Anal. Min.
,
3
(
4
), pp.
1149
1163
.
66.
Jin
,
W.
,
Ho
,
H. H.
, and
Rohini
,
K. S.
,
2009
, “
OpinionMiner: A Novel Machine Learning System for Web Opinion Mining and Extraction
,”
15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
(
KDD'09
), Paris, France, June 28–July 1, pp.
1195
1204
https://dl.acm.org/citation.cfm?id=1557148.
67.
Chen
,
L.
,
Qi
,
L.
, and
Wang
,
F.
,
2012
, “
Comparison of Feature-Level Learning Methods for Mining Online Consumer Reviews
,”
Expert Syst. Appl.
,
39
(
10
), pp.
9588
9601
.
68.
Rentoumi
,
V.
,
Vouros
,
G. A.
,
Karkaletsis
,
V.
, and
Moser
,
A.
,
2012
, “
Investigating Metaphorical Language in Sentiment Analysis: A Sense-to-Sentiment Perspective
,”
ACM Trans. Speech Language Process.
,
9
(
3
), p.
6
.
69.
McAuley
,
J. J.
,
Leskovec
,
J.
, and
Jurafsky
,
D.
,
2012
, “
Learning Attitudes and Attributes From Multi-Aspect Reviews
,”
IEEE 12th International Conference on Data Mining
(
ICDM'12
), Dec. 10–13, pp. 1020–1025https://dl.acm.org/citation.cfm?id=2472547.
70.
Moghaddam
,
S.
, and
Ester
,
M.
,
2013
, “
The FLDA Model for Aspect-Based Opinion Mining: Addressing the Cold Start Problem
,”
22nd International Conference on World Wide Web
(
WWW'13
), Rio de Janeiro, Brazil, May 13–17, pp.
909
918
https://dl.acm.org/citation.cfm?doid=2488388.2488467.
71.
Saad
,
F.
, and
Mathiak
,
B.
,
2013
, “
Revised Mutual Information Approach for German Text Sentiment Classification
,”
WWW'13 Companion
, Rio de Janeiro, Brazil, pp.
579
586
http://www2013.w3c.br/companion/p579.pdf.
72.
Kim
,
S.-M.
, and
Hovy
,
E.
,
2006
, “
Automatic Identification of Pro and Con Reasons in Online Reviews
,”
COLING/ACL on Main Conference Poster Sessions
(
COLING-ACL
), Sydney, Australia, July 17–18, pp.
483
490
https://dl.acm.org/citation.cfm?id=1273136.
73.
Yu
,
J.
,
Zha
,
Z.-J.
,
Wang
,
M.
, and
Chua
,
T.-S.
,
2011
, “
Aspect Ranking: Identifying Important Product Aspects From Online Consumer Reviews
,”
ACL'11
, Portland, OR, June 19–24, pp.
1496
1505
.
74.
Rao
,
Y.
,
Lei
,
J.
,
Wenyin
,
L.
,
Li
,
Q.
, and
Chen
,
M.
,
2013
, “
Building Emotional Dictionary for Sentiment Analysis of Online News
,” World Wide Web, pp.
1
20
.
75.
Qiu
,
L.
,
Zhang
,
W.
,
Hu
,
C.
, and
Zhao
,
K.
,
2009
, “
SELC: A Self-Supervised Model for Sentiment Classification
,”
18th ACM Conference on Information and Knowledge Management
(
CIKM'09
), Hong Kong, China, Nov. 2–6, pp.
929
936
https://dl.acm.org/citation.cfm?id=1646072.
76.
Zhang
,
W.
,
Ding
,
G.
,
Li Chen
,
C.
,
Li
,
C.
, and
Zhang
,
C.
,
2013
, “
Generating Virtual Ratings From Chinese Reviews to Augment Online Recommendations
,”
ACM Trans. Intell. Syst. Technol.
,
4
(
1
), p.
9
.
77.
Hu
,
M.
, and
Liu
,
B.
,
2004
, “
Mining and Summarizing Customer Reviews
,”
Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
(
KDD'04
), Seattle, WA, Aug. 22–25, pp.
168
177
https://dl.acm.org/citation.cfm?id=1014073.
78.
Liu
,
B.
,
Hu
,
M.
, and
Cheng
,
J.
,
2005
, “
Opinion Observer: Analyzing and Comparing Opinions on the Web
,”
14th international Conference on World Wide Web
(
WWW'05
), Chiba, Japan, May 10–14, pp. 342–351.https://dl.acm.org/citation.cfm?id=1060797
79.
Popescu
,
A.-M.
, and
Etzioni
,
O.
,
2005
, “
Extracting Product Features and Opinions From Reviews
,”
EMNLP'05
, pp.
339
346
.
80.
Hai
,
Z.
,
Chang
,
K.
, and
Cong
,
G.
,
2012
, “
One Seed to Find Them All: Mining Opinion Features Via Association
,”
21st ACM International Conference on Information and Knowledge Management
(
CIKM'12
), Maui, HI, Oct. 29–Nov. 2, pp.
255
264
https://dl.acm.org/citation.cfm?id=2396797.
81.
Stoyanov
,
V.
, and
Cardie
,
C.
,
2008
, “
Topic Identification for Fine-Grained Opinion Analysis
,”
COLING'08
, Manchester, UK, Aug. 18–22, pp.
817
824
.
82.
Guo
,
H.
,
Zhu
,
H.
,
Guo
,
Z.
,
Zhang
,
X. X.
, and
Su
,
Z.
,
2009
, “
Product Feature Categorization With Multilevel Latent Semantic Association
,”
18th ACM Conference on Information and Knowledge Management
(
CIKM'09
), Hong Kong, China, Nov. 2–6, pp.
1087
1096
https://dl.acm.org/citation.cfm?id=1646091.
83.
Lin
,
C.
, and
He
,
Y.
,
2009
, “
Joint Sentiment/Topic Model for Sentiment Analysis
,”
18th ACM Conference on Information and Knowledge Management
(
CIKM'09
), Hong Kong, China, Nov. 2–6, pp.
375
–3
84
https://dl.acm.org/citation.cfm?id=1646003&dl=ACM&coll=DL.
84.
Lin
,
C.
,
He
,
Y.
, and
Everson
,
R.
,
2010
, “
A Comparative Study of Bayesian Models for Unsupervised Sentiment Detection
,” 14th Conference on Computational Natural Language Learning (
CONLL'10
), Uppsala, Sweden, July 15–16, pp.
144
152
https://dl.acm.org/citation.cfm?id=1870586.
85.
Jo
,
Y.
, and
Oh
,
A. H.
,
2011
, “
Aspect and Sentiment Unification Model for Online Review Analysis
,”
Fourth ACM International Conference on Web Search and Data Mining
(
WSDM'11
), Hong Kong, China, Feb. 9–12, pp.
815
824
.https://dl.acm.org/citation.cfm?id=1935932
86.
Xu
,
X.
,
Tan
,
S.
,
Liu
,
Y.
,
Cheng
,
X.
, and
Lin
,
Z.
,
2012
, “
Towards Jointly Extracting Aspects and Aspect-Specific Sentiment Knowledge
,”
21st ACM International Conference on Information and Knowledge Management
(
CIKM'12
), Maui, HI, Oct. 29–Nov. 2, pp.
18
19
https://dl.acm.org/citation.cfm?id=2396761.2398539.
87.
Titov
,
I.
, and
McDonald
,
R.
,
2008
, “
Modeling Online Reviews With Multi-Grain Topic Models
,”
17th International Conference on World Wide Web
(
WWW'08
), Beijing, China, Apr. 21–25, pp.
111
120
https://dl.acm.org/citation.cfm?id=1367513.
88.
Chen
,
R.
, and
Xu
,
W.
,
2017
, “
The Determinants of Online Customer Ratings: A Combined Domain Ontology and Topic Text Analytics Approach
,”
Electron. Commerce Res.
,
17
(
1
), pp.
31
50
.
89.
Alam
,
M. H.
, and
Lee
,
S. K.
,
2012
, “
Semantic Aspect Discovery for Online Reviews
,”
IEEE 12th International Conference on Data Mining
(
ICDM'12
), Brussels, Belgium, Dec. 10–13, pp.
816
821
.
90.
Mukherjee
,
A.
, and
Liu
,
B.
,
2012a
, “
Aspect Extraction Through Semi-Supervised Modeling
,”
50th Annual Meeting of the Association for Computational Linguistics
(
ACL'12
), Jeju Island, South Korea, July 8–14, pp.
339
348
https://dl.acm.org/citation.cfm?id=2390572.
91.
Moghaddam
,
S.
, and
Ester
,
M.
,
2011
, “
ILDA: Interdependent LDA Model for Learning Latent Aspects and Their Ratings From Online Product Reviews
,”
SIGIR'11
, Beijing, China, pp.
665
674
.
92.
Moghaddam
,
S.
, and
Ester
,
M.
,
2012
, “
On the Design of LDA Models for Aspect-Based Opinion Mining
,” 21st ACM International Conference on Information and Knowledge Management (
CIKM'12
), Maui, HI, Oct. 29–Nov. 2, pp.
803
812
.https://dl.acm.org/citation.cfm?id=2396863
93.
Yang
,
C. C.
,
Wong
,
Y. C.
, and
Wei
,
C.-P.
,
2009
, “
Classifying Web Review Opinions for Consumer Product Analysis
,”
11th International Conference on Electronic Commerce
(
ICEC'09
), Taipei, Taiwan, Aug. 12–15, pp.
57
63
https://dl.acm.org/citation.cfm?id=1593263.
94.
Zhai
,
Z.
,
Liu
,
B.
,
Xu
,
H.
, and
Jia
,
P.
,
2010
, “
Grouping Product Features Using Semi-Supervised Learning With Soft-Constraints
,”
23rd International Conference on Computational Linguistics
(
COLING'10
), Beijing, China, Aug. 23–27, pp.
1272
80
https://dl.acm.org/citation.cfm?id=1873924.
95.
Yang
,
C.-S.
,
Wei
,
C.-P.
, and
Christopher
,
C. Y.
,
2009
, “
Extracting Customer Knowledge From Online Consumer Reviews: A Collaborative-Filtering-Based Opinion Sentence Identification Approach
,”
11th International Conference on Electronic Commerce
(
ICEC'09
), Taipei, Taiwan, Aug. 12–15, pp.
64
71
.
96.
Zhu
,
J.
,
Wang
,
H.
,
Benjamin
,
K. T.
, and
Zhu
,
M.
,
2009
, “
Multi-Aspect Opinion Polling From Textual Reviews
,”
18th ACM Conference on Information and Knowledge Management
(
CIKM'09
), Hong Kong, China, Nov. 2–6, pp.
1799
1802
https://dl.acm.org/citation.cfm?id=1646233.
97.
Su
,
Q.
,
Xu
,
X.
,
Guo
,
H.
,
Guo
,
Z.
,
Wu
,
X.
,
Zhang
,
X.
,
Swen
,
B.
, and
Su
,
Z.
,
2008
, “
Hidden Sentiment Association in Chinese Web Opinion Mining
,”
17th International Conference on World Wide Web
(
WWW'08
), Beijing, China, Apr. 21–25, pp.
959
968
https://dl.acm.org/citation.cfm?id=1367627.
98.
Kim
,
J.
,
Li
,
J.-J.
, and
Lee
,
J.-H.
,
2009
, “
Discovering the Discriminative Views: Measuring Term Weights for Sentiment Analysis
,”
ACL'09
, Suntec, Singapore, Aug. 2–7, pp.
253
261
.https://dl.acm.org/citation.cfm?id=1687915
99.
Esuli
,
A.
, and
Sebastiani
,
F.
,
2006
, “
Determining Term Subjectivity and Term Orientation for Opinion Mining
,”
EACL'06
, Stanford, CA, pp. 193–200https://pdfs.semanticscholar.org/af5c/4034493461af13a7f5480e081becf0218511.pdf.
100.
Wiebe
,
J.
,
Wilson
,
T.
, and
Cardie
,
C.
,
2005
, “
Annotating Expressions of Opinions and Emotions in Language
,”
Language Resour. Eval.
,
39
(
2–3
), pp.
165
210
.
101.
Zhang
,
W.
,
Yu
,
C.
, and
Meng
,
W.
,
2007
, “
Opinion Retrieval From Blogs
,”
Sixteenth ACM Conference on Information and Knowledge Management
(
CIKM'07
), Lisbon, Portugal, Nov. 6–10, pp.
831
840
https://dl.acm.org/citation.cfm?id=1321555.
102.
Ganesan
,
K.
, and
Zhai
,
C.
,
2012
, “
FindiLike: Preference Driven Entity Search
,”
WWW'12 Companion
, Lyon, France, Apr. 16–20, pp.
345
348
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.309.9932&rep=rep1&type=pdf.
103.
Gerani
,
S.
,
Carman
,
M. J.
, and
Crestani
,
F.
,
2010
, “
Proximity-Based Opinion Retrieval
,”
3rd International ACM SIGIR Conference on Research and Development in Information Retrieval
(
SIGIR'10
), Geneva, Switzerland, July 19–23, pp.
403
410
https://dl.acm.org/citation.cfm?id=1835517.
104.
Gerani
,
S.
,
Carman
,
M.
, and
Crestani
,
F.
,
2012
, “
Aggregation Methods for Proximity-Based Opinion Retrieval
,”
ACM Trans. Inf. Syst.
,
30
(
4
), pp.
26:1–26
36
.
105.
Huang
,
S.
,
Shen
,
D.
,
Feng
,
W.
,
Baudin
,
C.
, and
Zhang
,
Y.
,
2009
, “
Improving Product Review Search Experiences on General Search Engines
,”
11th International Conference on Electronic Commerce
(
ICEC'09
), Taipei, Taiwan, pp.
107
116
.
106.
Wan
,
X.
,
2008
, “
Using Bilingual Knowledge and Ensemble Techniques for Unsupervised Chinese Sentiment Analysis
,”
Conference on Empirical Methods in Natural Language Processing
(
EMNLP'08
), Honolulu, HI, Oct. 25–27, pp.
553
561
https://dl.acm.org/citation.cfm?id=1613783.
107.
Wan
,
X.
,
2009
, “
Co-Training for Cross-Lingual Sentiment Classification
,”
ACL'09
, Suntec, Singapore, Aug. 2–7, pp.
235
243
https://dl.acm.org/citation.cfm?id=1687913.
108.
Wan
,
X.
,
2011
, “
Bilingual Co-Training for Sentiment Classification of Chinese Product Reviews
,”
Comput. Linguist.
,
37
(
3
), pp.
587
616
.
109.
Wan
,
X.
,
2012
, “
A Comparative Study of Cross-Lingual Sentiment Classification
,”
IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology
(
Wi-Iat'12
), Macau, China, Dec. 4–7, pp.
24
31
.
110.
Guo
,
H.
,
Zhu
,
H.
,
Guo
,
Z.
,
Zhang
,
X.
, and
Su
,
Z.
,
2010
, “
OpinionIt: A Text Mining System for Cross-Lingual Opinion Analysis
,”
19th ACM International Conference on Information and Knowledge Management
(
CIKM'10
), Toronto, ON, Canada, Oct. 26–30, pp.
1199
1208
https://dl.acm.org/citation.cfm?id=1871589&dl=ACM&coll=DL.
111.
Abbasi
,
A.
,
Chen
,
H.
, and
Salem
,
A.
,
2008
, “
Sentiment Analysis in Multiple Languages: Feature Selection for Opinion Classification in Web Forums
,”
ACM Trans. Inf. Syst.
,
26
(
3
), p.
12
.
112.
Lin
,
Z.
,
Tan
,
S.
, and
Cheng
,
X.
,
2012
, “
A Fast and Accurate Method for Bilingual Opinion Lexicon Extraction
,”
IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology
(
Wi-Iat'12
), Macau, China, Dec. 4–7, pp.
50
57
.
113.
Yu
,
J.
,
Zha
,
Z.-J.
,
Wang
,
M.
, and
Chua
,
T.-S.
,
2011b
, “
Hierarchical Organization of Unstructured Consumer Reviews
,”
WWW'11
, Hyderabad, India, Mar. 28–Apr. 1, pp.
171
172
https://www.researchgate.net/publication/221022180_Hierarchical_organization_of_unstructured_consumer_reviews.
114.
Yu
,
J.
,
Zha
,
Z.-J.
,
Wang
,
M.
,
Wang
,
K.
, and
Chua
,
T.-S.
,
2011
, “
Domain-Assisted Product Aspect Hierarchy Generation: Towards Hierarchical Organization of Unstructured Consumer Reviews
,”
Conference on Empirical Methods in Natural Language Processing
(
EMNLP'11
), Edinburgh, UK, July 27–31, pp.
140
150
https://dl.acm.org/citation.cfm?id=2145432.2145449.
115.
Yu
,
J.
,
Zha
,
Z.-J.
, and
Chua
,
T.-S.
,
2012
, “
Answering Opinion Questions on Products by Exploiting Hierarchical Organization of Consumer Reviews
,”
EMNLP-Conll'12
, Jeju Island, South Korea, pp.
391
401
http://www.aclweb.org/anthology/D12-1036.
116.
Zhai
,
Z.
,
Liu
,
B.
,
Xu
,
H.
, and
Jia
,
P.
,
2011
, “
Clustering Product Features for Opinion Mining
,”
Fourth ACM International Conference on Web Search and Data Mining
(
WSDM'11
), Hong Kong, China, Feb. 9–12, pp.
347
354
https://dl.acm.org/citation.cfm?id=1935884.
117.
Li
,
F.
,
Han
,
C.
,
Huang
,
M.
,
Zhu
,
X.
,
Xia
,
Y.-J.
,
Zhang
,
S.
, and
Yu
,
H.
,
2010
, “
Structure-Aware Review Mining and Summarization
,”
23rd International Conference on Computational Linguistics
(
Coling 2010
), Beijing, China, July 15–16, pp.
653
661
http://delivery.acm.org/10.1145/1880000/1873855/p653-li.pdf?ip=182.74.252.242&id=1873855&acc=OPEN&key=4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35%2E6D218144511F3437&__acm__=1535029438_9f323933f1625e2edd334aa5ffc86ed3.
118.
Lu
,
Y.
,
Zhai
,
C. X.
, and
Sundaresan
,
N.
,
2009
, “
Rated Aspect Summarization of Short Comments
,”
18th International Conference on World Wide Web
(
WWW'09
), Madrid, Spain, Apr. 20–24, pp.
131
140
https://dl.acm.org/citation.cfm?id=1526728.
119.
Ganesan
,
K.
,
Zhai
,
C. X.
, and
Viegas
,
E.
,
2012
, “
Micropinion Generation: An Unsupervised Approach to Generating Ultra-Concise Summaries of Opinions
,”
WWW'12
, Lyon, France, pp.
869
878
.
120.
Zhuang
,
L.
,
Jing
,
F.
, and
Zhu
,
X. Y.
,
2006
, “
Movie Review Mining and Summarization
,”
15th ACM International Conference on Information and Knowledge Management
(
CIKM'06
), Arlington, VA, Nov. 6–11, pp.
43
50
https://dl.acm.org/citation.cfm?id=1183625.
121.
Ly
,
D. K.
,
Sugiyama
,
K.
,
Lin
,
Z.
, and
Kan
,
M.-Y.
,
2011
, “
Product Review Summarization From a Deeper Perspective
,” 11th Annual International ACM/IEEE Joint Conference on Digital Libraries (
JCDL'11
), Ottawa, ON, Canada, June 13–17, pp.
311
14
https://dl.acm.org/citation.cfm?id=1998076.1998134.
122.
Yatani
,
K.
,
Novati
,
M.
,
Trusty
,
A.
, and
Khai
,
N. T.
,
2011
, “
Review Spotlight: A User Interface for Summarizing User-Generated Reviews Using Adjective-Noun Word Pairs
,”
SIGCHI Conference on Human Factors in Computing Systems
(
CHI'11
), Vancouver, BC, Canada, May 7–12, pp.
1541
1550
https://dl.acm.org/citation.cfm?id=1979167&dl=ACM&coll=DL.
123.
Rohrdantz
,
C.
,
Hao
,
M. C.
,
Dayal
,
U.
,
Haug
,
L.-E.
, and
Keim
,
D. A.
,
2012
, “
Feature-Based Visual Sentiment Analysis of Text Document Streams
,”
ACM Trans. Intell. Syst. Technol.
,
3
(
2
), p.
26
124.
Das
,
A.
, and
Bandyopadhyay
,
S.
,
2010
, “
Topic-Based Bengali Opinion Summarization
,”
COLING'10
, Beijing, China, pp.
232
240
.
125.
Ma
,
Z.
,
Sun
,
A.
,
Yuan
,
Q.
, and
Cong
,
G.
,
2012
, “
Topic-Driven Reader Comments Summarization
,”
21st ACM International Conference on Information and Knowledge Management
(
CIKM'12
), Maui, HI, Oct. 29–Nov. 2, pp.
265
274
https://dl.acm.org/citation.cfm?id=2396761.2396798.
126.
Ju
,
S.
,
Li
,
S.
,
Su
,
Y.
,
Zhou
,
G.
,
Hong
,
Y.
, and
Li
,
X.
,
2012
, “
Dual Word and Document Seed Selection for Semi-Supervised Sentiment Classification
,”
CIKM'12
, Maui, HI, pp.
2295
2298
.
127.
Tsaparas
,
P.
,
Ntoulas
,
A.
, and
Terzi
,
E.
,
2011
, “
Selecting a Comprehensive Set of Reviews
,”
17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
(
KDD'11
), San Diego, CA, Aug. 21–24, pp.
168
176
https://dl.acm.org/citation.cfm?id=2020440.
128.
Lappas
,
T.
,
Crovella
,
M.
, and
Terzi
,
E.
,
2012
, “
Selecting a Characteristic Set of Reviews
,”
KDD'12
, Beijing, China, pp.
832
840
.
129.
Li
,
S.
,
Ju
,
S.
,
Zhou
,
G.
, and
Li
,
X.
,
2012
, “
Active Learning for Imbalanced Sentiment Classification
,”
Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
(
EMNLP-Conll'12
), Jeju Island, South Korea, July 12–14, pp.
139
148
https://dl.acm.org/citation.cfm?id=2390966.
130.
Agarwal
,
D.
,
Chen
,
B.-C.
, and
Pang
,
B.
,
2011
, “
Personalized Recommendation of User Comments Via Factor Models
,”
Conference on Empirical Methods in Natural Language Processing
(
EMNLP'11
), Edinburgh, UK, July 27–31, pp.
571
582
https://dl.acm.org/citation.cfm?id=2145499.
131.
Moghaddam
,
S.
,
Jamali
,
M.
, and
Ester
,
M.
,
2011
, “
Review Recommendation: Personalized Prediction of the Quality of Online Reviews
,”
20th ACM International Conference on Information and Knowledge Management
(
CIKM'11
), Glasgow, UK, Oct. 24–28, pp.
2249
2252
https://dl.acm.org/citation.cfm?id=2063938&dl=ACM&coll=DL.
132.
Moghaddam
,
S.
,
Jamali
,
M.
, and
Ester
,
M.
,
2012
, “
ETF: Extended Tensor Factorization Model for Personalizing Prediction of Review Helpfulness
,” Fifth ACM International Conference on Web Search and Data Mining (
WSDM'12
), Seattle, WA, Feb. 8–12, pp. 163–172https://dl.acm.org/citation.cfm?id=2124316.
133.
Lu
,
Y.
,
Wang
,
H.
,
Zhai
,
C.
, and
Roth
,
D.
,
2012
, “
Unsupervised Discovery of Opposing Opinion Networks From Forum Discussions
,”
CIKM'12
, Maui, HI, Oct. 29–Nov. 2, pp.
1642
1646
https://dl.acm.org/citation.cfm?doid=2396761.2398489.
134.
Fang
,
Y.
,
Si
,
L.
,
Somasundaram
,
N.
, and
Yu
,
Z.
,
2012
, “
Mining Contrastive Opinions on Political Texts Using Cross-Perspective Topic Model
,”
WSDM'12
, Seattle, WA, Feb. 8–12, pp.
63
72
https://dl.acm.org/citation.cfm?id=2124306.
135.
Mukherjee
,
A.
, and
Liu
,
B.
,
2012
, “
Mining Contentions From Discussions and Debates
,”
KDD'12
, Beijing, China, pp.
841
849
.
136.
Ganapathibhotla
,
M.
, and
Liu
,
B.
,
2008
, “
Mining Opinions in Comparative Sentences
,”
22nd International Conference on Computational Linguistic
(
COLING'08
), Manchester, UK, Aug. 18–22, pp.
241
–2
48
https://dl.acm.org/citation.cfm?id=1599112.
137.
Jindal
,
N.
, and
Liu
,
B.
,
2006
, “
Identifying Comparative Sentences in Text Documents
,”
SIGIR'06
, Seattle, WA, Aug. 6–11, pp.
244
251
https://dl.acm.org/citation.cfm?id=1148215.
138.
Xu
,
K.
,
Liao
,
S. S.
,
Li
,
J.
, and
Song
,
Y.
,
2011
, “
Mining Comparative Opinions From Customer Reviews for Competitive Intelligence
,”
Decis. Support Syst.
,
50
(
4
), pp.
743
754
.
139.
Paul
,
M. J.
,
Zhai
,
C.
, and
Girju
,
R.
,
2010
, “
Summarizing Contrastive Viewpoints in Opinionated Text
,”
EMNLP'10
, Cambridge, MA, Oct. 9–11, pp.
66
76
.https://dl.acm.org/citation.cfm?id=1870665
140.
Kim
,
H. D.
, and
Zhai
,
C.
,
2009
, “
Generating Comparative Summaries of Contradictory Opinions in Text
,”
18th ACM Conference on Information and Knowledge Management
(
CIKM'09
), Hong Kong, China, Nov. 2–6, pp.
385
394
.https://dl.acm.org/citation.cfm?id=1646004
141.
Zhang
,
K.
,
Narayanan
,
R.
, and
Choudhary
,
A.
,
2010
, “
Voice of the Customers: Mining Online Customer Reviews for Product Feature-Based Ranking
,”
Third Conference on Online Social Networks
(
WOSN'10
), Boston, MA, June 22–25, pp.
1
9
https://dl.acm.org/citation.cfm?id=1863201.
142.
McAuley
,
J. J.
, and
Leskovec
,
J.
,
2013
, “
Hidden Factors and Hidden Topics: Understanding Rating Dimensions With Review Text
,”
RecSys'13
, Hong Kong, China, Oct. 12–16, pp.
165
172
https://dl.acm.org/citation.cfm?id=2507163.
143.
Raghavan
,
S.
,
Gunasekar
,
S.
, and
Ghosh
,
J.
,
2012
, “
Review Quality Aware Collaborative Filtering
,”
RecSys'12
, Dublin, Ireland, Sept. 9–13, pp.
123
130
https://dl.acm.org/citation.cfm?id=2365978.
144.
Zhang
,
K.
,
Cheng
,
Y.
,
Liao
,
W.-K.
, and
Choudhary
,
A.
,
2012
, “
Mining Millions of Reviews: A Technique to Rank Products Based on Importance of Reviews
,”
13th International Conference on Electronic Commerce
(
ICEC'11
), Liverpool, UK, Aug. 3–5, pp.
12:1
12:8
https://dl.acm.org/citation.cfm?id=2378116.
145.
Stavrianou
,
A.
, and
Brun
,
C.
,
2012
, “
Opinion and Suggestion Analysis for Expert Recommendations
,” Workshop on Semantic Analysis in Social Media (
EACL
), Avignon, France, Apr. 23, pp.
61
69
https://dl.acm.org/citation.cfm?id=2389977.
146.
McAuley
,
J. J.
, and
Leskovec
,
J.
,
2013
, “
From Amateurs to Connoisseurs: Modeling the Evolution of User Expertise Through Online Reviews
,”
22nd International Conference on World Wide Web
(
WWW'13
), Rio de Janeiro, Brazil, May 13–17, pp.
897
908
https://dl.acm.org/citation.cfm?id=2488466.
147.
Xu
,
Y.
,
Lam
,
W.
, and
Lin
,
T.
,
2014
, “
Collaborative Filtering Incorporating Review Text and Co-Clusters of Hidden User Communities and Item Groups
,”
23rd ACM International Conference on Information and Knowledge Management
(
CIKM'14
), Shanghai, China, Nov. 3–7, pp.
251
260
https://dl.acm.org/citation.cfm?id=2662059.
148.
Zhao
,
W. X.
,
Wang
,
J.
,
He
,
Y.
,
Wen
,
J.-R.
,
Chang
,
E. Y.
, and
Li
,
X.
,
2016
, “
Mining Product Adopter Information From Online Reviews for Improving Product Recommendation
,”
ACM Trans. Knowl. Discovery Data
,
10
(
3
), p.
29
.
149.
Ma
,
Y.
,
Chen
,
G.
, and
Wei
,
Q.
,
2017
, “
Does Big Data Mean Big Knowledge? Integration of Big Data Analysis and Conceptual Model for Social Commerce Research
,”
Electron. Commerce Res.
,
17
(
1
), pp.
3
29
.
150.
Hu
,
N.
,
Liu
,
L.
, and
Zhang
,
J. J.
,
2008
, “
Do Online Reviews Affect Product Sales? The Role of Reviewer Characteristics and Temporal Effects
,”
Inf. Technol. Manage.
,
9
(
3
), pp.
201
214
.
151.
Zhang
,
Z.
,
Li
,
X.
, and
Chen
,
Y.
,
2012
, “
Deciphering Word-of-Mouth in Social Media: Text-Based Metrics of Consumer Reviews
,”
ACM Trans. Manage. Inf. Syst.
,
3
(
1
), pp.
5:1–5
:
23
.
152.
Archak
,
N.
,
Ghose
,
A.
, and
Panagiotis
,
G. I.
,
2007
, “
Show Me the Money!: Deriving the Pricing Power of Product Features by Mining Consumer Reviews
,”
13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
(
KDD'07
), San Jose, CA, Aug. 12–15, pp.
56
65
https://dl.acm.org/citation.cfm?id=1281202.
153.
Archak
,
N.
,
Ghose
,
A.
, and
Ipeirotis
,
P. G.
,
2011
, “
Deriving the Pricing Power of Product Features by Mining Consumer Reviews
,”
Manage. Sci.
,
57
(
8
), pp.
1485
1509
.
154.
Liu
,
Y.
,
Yu
,
X.
,
Huang
,
X.
, and
An
,
A.
,
2010
, “
S-PLASA+: Adaptive Sentiment Analysis With Application to Sales Performance Prediction
,”
SIGIR'10
, Geneva, Switzerland, pp.
873
874
.
155.
Chan
,
L.-K.
, and
Wu
,
M.-L.
,
2002
, “
Quality Function Deployment: A Literature Review
,”
Eur. J. Oper. Res.
,
143
(
3
), pp.
463
497
.
156.
Aboulafia
,
A.
, and
Bannon
,
L. J.
,
2004
, “
Understanding Affect in Design: An Outline Conceptual Framework
,”
Theor. Issues Ergonom. Sci.
,
5
(
1
), pp.
4
15
.
157.
Barnes
,
C.
, and
Lillford
,
S. P.
,
2009
, “
Decision Support for the Design of Affective Products
,”
J. Eng. Des.
,
20
(
5
), pp.
477
492
.
158.
Chen
,
C.-H.
,
Khoo
,
L. P.
, and
Yan
,
W.
,
2006
, “
An Investigation Into Affective Design Using Sorting Technique and Kohonen Self-Organising Map
,”
Adv. Eng. Software
,
37
(
5
), pp.
334
349
.
159.
Kwong
,
C. K.
,
Jiang
,
H.
, and
Luo
,
X. G.
,
2016
, “
AI-Based Methodology of Integrating Affective Design, Engineering, and Marketing for Defining Design Specifications of New Products
,”
Eng. Appl. Artif. Intell.
,
47
(
Suppl. C
), pp.
49
60
.
160.
Khalid
,
H. M.
,
2006
, “
Embracing Diversity in User Needs for Affective Design
,”
Appl. Ergonom.
,
37
(
4
), pp.
409
418
.
161.
Hsu
,
F.-C.
,
Lin
,
Y.-H.
, and
Chen
,
C.-N.
,
2015
, “
Applying Cluster Analysis for Consumer's Affective Responses Toward Product Forms
,”
J. Interdiscip. Math.
,
18
(
6
), pp.
657
666
.
162.
Balters
,
S.
, and
Steinert
,
M.
,
2017
, “
Capturing Emotion Reactivity Through Physiology Measurement as a Foundation for Affective Engineering in Engineering Design Science and Engineering Practices
,”
J. Intell. Manuf.
,
28
(
7
), pp.
1585
1607
.
163.
Diego-Mas
,
J. A.
, and
Alcaide-Marzal
,
J.
,
2016
, “
Single Users' Affective Responses Models for Product Form Design
,”
Int. J. Ind. Ergonom.
,
53
(
Suppl. C
), pp.
102
114
.
164.
Abegaz
,
T.
,
Dillon
,
E.
, and
Gilbert
,
J. E.
,
2015
, “
Exploring Affective Reaction During User Interaction With Colors and Shapes
,”
Procedia Manuf.
, 3(Suppl. C), pp.
5253
5260
.
165.
Fung
,
C. K. Y.
,
Kwong
,
C. K.
,
Chan
,
K. Y.
, and
Jiang
,
H.
,
2014
, “
A Guided Search Genetic Algorithm Using Mined Rules for Optimal Affective Product Design
,”
Eng. Optim.
,
46
(
8
), pp.
1094
1108
.
166.
Lo
,
C.-H.
, and
Chu
,
C.-H.
,
2009
, “
Experimental Study for Computer Aided Affective Product Styling
,”
Comput.-Aided Des. Appl.
,
6
(
4
), pp.
471
482
.
167.
Jiang
,
H.
,
Kwong
,
C. K.
,
Siu
,
K. W. M.
, and
Liu
,
Y.
,
2015
, “
Rough Set and Pso-Based Anfis Approaches to Modeling Customer Satisfaction for Affective Product Design
,”
Adv. Eng. Inf.
,
29
(
3
), pp.
727
738
.
168.
Jiang
,
H.
,
Kwong
,
C. K.
,
Ying
,
L.
, and
Ip
,
W. H.
,
2015
, “
A Methodology of Integrating Affective Design With Defining Engineering Specifications for Product Design
,”
Int. J. Prod. Res.
,
53
(
8
), pp.
2472
2488
.
169.
Seva
,
R. R.
,
Katherine Grace
,
T.
,
Gosiaco
,
M.
,
Crea Eurice
,
D.
,
Santos
,
D. M. L.
, and
Pangilinan
,
2011
, “
Product Design Enhancement Using Apparent Usability and Affective Quality
,”
Appl. Ergonom.
,
42
(
3
), pp.
511
517
.
170.
Chan
,
K. Y.
, and
Engelke
,
U.
,
2017
, “
Varying Spread Fuzzy Regression for Affective Quality Estimation
,”
IEEE Trans. Fuzzy Syst.
,
25
(
3
), pp.
594
613
.
171.
Jiao
,
R. J.
,
Xu
,
Q.
,
Du
,
J.
,
Zhang
,
Y.
,
Helander
,
M.
,
Khalid
,
H. M.
,
Helo
,
P.
, and
Ni
,
C.
,
2007
, “
Analytical Affective Design With Ambient Intelligence for Mass Customization and Personalization
,”
Int. J. Flexible Manuf. Syst.
,
19
(
4
), pp.
570
595
.
172.
Ling
,
S. H.
,
San
,
P. P.
,
Chan
,
K. Y.
,
Leung
,
F. H. F.
, and
Liu
,
Y.
,
2014
, “
An Intelligent Swarm Based-Wavelet Neural Network for Affective Mobile Phone Design
,”
Neurocomputing
,
142
(
Suppl. C
), pp.
30
38
.
173.
Kim
,
H. K.
,
Han
,
S. H.
,
Park
,
J.
, and
Park
,
J.
,
2016
, “
Identifying Affect Elements Based on a Conceptual Model of Affect: A Case Study on a Smartphone
,”
Int. J. Ind. Ergonom.
,
53
(
Suppl. C
), pp.
193
204
.
174.
Akay
,
D.
, and
Kurt
,
M.
,
2008
, “
A Neuro-Fuzzy Based Approach to Affective Design
,”
Int. J. Adv. Manuf. Technol.
,
40
(
5
), p.
425
.
175.
Bruch
,
J.
, and
Bellgran
,
M.
,
2013
, “
Characteristics Affecting Management of Design Information in the Production System Design Process
,”
Int. J. Prod. Res.
,
51
(
11
), pp.
3241
3251
.
176.
Lu
,
W.
, and
Jean-Francois
,
P.
,
2014
, “
Affective Design of Products Using an Audio-Based Protocol: Application to Eyeglass Frame
,”
Int. J. Ind. Ergonom.
,
44
(
3
), pp.
383
394
.
177.
Zhou
,
F.
,
Ji
,
Y.
, and
Jiao
,
R. J.
,
2013
, “
Affective and Cognitive Design for Mass Personalization: Status and Prospect
,”
J. Intell. Manuf.
,
24
(
5
), pp.
1047
1069
.
178.
Zhou
,
F.
,
Ji
,
Y.
, and
Jiao
,
R. J.
,
2014
, “
Prospect-Theoretic Modeling of Customer Affective-Cognitive Decisions Under Uncertainty for User Experience Design
,”
IEEE Trans. Human-Mach. Syst.
,
44
(
4
), pp.
468
483
.
179.
Jiao
,
R. J.
,
Zhou
,
F.
, and
Chu
,
C.-H.
,
2017
, “
Decision Theoretic Modeling of Affective and Cognitive Needs for Product Experience Engineering: Key Issues and a Conceptual Framework
,”
J. Intell. Manuf.
,
28
(
7
), pp.
1755
1767
.
180.
Zhou
,
F.
,
Lei
,
B.
,
Liu
,
Y.
, and
Jiao
,
R. J.
,
2017
, “
Affective Parameter Shaping in User Experience Prospect Evaluation Based on Hierarchical Bayesian Estimation
,”
Expert Syst. with Appl.
,
78
(
Suppl. C
), pp.
1
15
.
181.
Olvander
,
J.
,
Bjorn
,
L.
, and
Gavel
,
H.
,
2009
, “
A Computerized Optimization Framework for the Morphological Matrix Applied to Aircraft Conceptual Design
,”
Comput.-Aided Des.
,
41
(
3
), pp.
187
196
.
182.
Ostertag
,
O.
,
Ostertagova
,
E.
, and
Robert
,
H.
,
2012
, “
Morphological Matrix Applied Within the Design Project of the Manipulator Frame
,”
Procedia Eng.
,
48
(
Suppl. C
), pp.
495
499
.
183.
He
,
B.
,
Song
,
W.
, and
Wang
,
Y.
, “
Computational Conceptual Design Using Space Matrix
,”
ASME J. Comput. Inf. Sci. Eng.
,
15
(
1
), p.
011004
.
184.
Ma
,
H.
,
Chu
,
X.
,
Xue
,
D.
, and
Chen
,
D.
,
2017
, “
A Systematic Decision Making Approach for Product Conceptual Design Based on Fuzzy Morphological Matrix
,”
Expert Syst. Appl.
,
81
(
Suppl. C
), pp.
444
456
.
185.
Yuan
,
L.
,
Liu
,
Y.
,
Lin
,
Y.
, and
Zhao
,
J.
,
2017
, “
An Automated Functional Decomposition Method Based on Morphological Changes of Material Flows
,”
J. Eng. Des.
,
28
(
1
), pp.
47
75
.
186.
Matthews
,
P. C.
,
2008
, “
A Bayesian Support Tool for Morphological Design
,”
Adv. Eng. Inf.
,
22
(
2
), pp.
236
253
.
187.
Lo
,
C.-H.
,
Tseng
,
K. C.
, and
Chu
,
C.-H.
,
2010
, “
One-Step Qfd Based 3D Morphological Charts for Concept Generation of Product Variant Design
,”
Expert Syst. Appl.
,
37
(
11
), pp.
7351
7363
.
188.
Fiorineschi
,
L.
,
Rotini
,
F.
, and
Rissone
,
P.
,
2016
, “
A New Conceptual Design Approach for Overcoming the Flaws of Functional Decomposition and Morphology
,”
J. Eng. Des.
,
27
(
7
), pp.
438
468
.
189.
Kroll
,
E.
,
2013
, “
Design Theory and Conceptual Design: Contrasting Functional Decomposition and Morphology With Parameter Analysis
,”
Res. Eng. Des.
,
24
(
2
), pp.
165
183
.
190.
Jimeno-Morenilla
,
A.
,
Molina-Carmona
,
R.
, and
Sanchez-Romero
,
J.-L.
,
2011
, “
Mathematical Morphology for Design and Manufacturing
,”
Math. Comput. Modell.
,
54
(
7–8
), pp.
1753
1759
.
191.
Kim
,
S.-J.
, and
Lee
,
J.-H.
,
2015
, “
Parametric Shape Modification and Application in a Morphological Biomimetic Design
,”
Adv. Eng. Inf.
,
29
(
1
), pp.
76
86
.
192.
Mintchev
,
S.
, and
Floreano
,
D.
,
2016
, “
Adaptive Morphology: A Design Principle for Multimodal and Multifunctional Robots
,”
IEEE Rob. Autom. Mag.
,
23
(
3
), pp.
42
54
.
193.
Chen
,
C.-H.
,
Khoo
,
L. P.
, and
Yan
,
W.
,
2002
, “
A Strategy for Acquiring Customer Requirement Patterns Using Laddering Technique and ART2 Neural Network
,”
Adv. Eng. Inf.
,
16
(
3
), pp.
229
240
.
194.
Griffin
,
A.
, and
Hauser
,
J. R.
,
1993
, “
The Voice of the Customer
,”
Marketing Sci.
,
12
(
1
), pp.
1
27
.
195.
Gustafsson
,
A.
, and
Gustafsson
,
N.
,
1994
, “
Exceeding Customer Expectations
,”
Sixth Symposium on Quality Function Deployment
, pp.
52
57
.
196.
Han
,
C. H.
,
Kim
,
J. K.
, and
Choi
,
S. H.
,
2004
, “
Prioritizing Engineering Characteristics in Quality Function Deployment With Incomplete Information: A Linear Partial Ordering Approach
,”
Int. J. Prod. Econ.
,
91
(
3
), pp.
235
249
.
197.
Wu
,
H.-H.
,
Liao
,
A. Y. H.
, and
Wang
,
P.-C.
,
2005
, “
Using Grey Theory in Quality Function Deployment to Analyse Dynamic Customer Requirements
,”
Int. J. Adv. Manuf. Technol.
,
25
(
11–12
), pp.
1241
1247
.
198.
Wu
,
H.-H.
, and
Shieh
,
J.-I.
,
2006
, “
Using a Markov Chain Model in Quality Function Deployment to Analyse Customer Requirements
,”
Int. J. Adv. Manuf. Technol.
,
30
(
1–2
), pp.
141
146
.
199.
Lai
,
X.
,
Xie
,
M.
,
Tan
,
K.-C.
, and
Yang
,
B.
,
2008
, “
Ranking of Customer Requirements in a Competitive Environment
,”
Comput. Ind. Eng.
,
54
(
2
), pp.
202
214
.
200.
Saaty
,
T. L.
,
1980
,
The Analytic Hierarchy Process
,
McGraw-Hill
, New York.
201.
Armacost
,
R. L.
,
Componation
,
P. J.
,
Mullens
,
M. A.
, and
Swart
,
W. W.
,
1994
, “
An AHP Framework for Prioritizing Customer Requirements in QFD: An Industrialized Housing Application
,”
IIE Trans.
,
26
(
4
), pp.
72
79
.
202.
Chuang
,
P.-T.
,
2001
, “
Combining the Analytic Hierarchy Process and Quality Function Deployment for a Location Decision From a Requirement Perspective
,”
Int. J. Adv. Manuf. Technol.
,
18
(
11
), pp.
842
849
.
203.
Nepal
,
B.
,
Yadav
,
O. P.
, and
Murat
,
A.
,
2010
, “
A Fuzzy-AHP Approach to Prioritization of CS Attributes in Target Planning for Automotive Product Development
,”
Expert Syst. Appl.
,
37
(
10
), pp.
6775
6786
.
204.
Fung
,
R.
,
Popplewell
,
Y. K. K.
, and
Xie
,
J.
,
1998
, “
An Intelligent Hybrid System for Customer Requirements Analysis and Product Attribute Targets Determination
,”
Int. J. Prod. Res.
,
36
(
1
), pp.
13
34
.
205.
Wang
,
H.
,
Xie
,
M.
, and
Goh
,
T. N.
,
1998
, “
A Comparative Study of the Prioritization Matrix Method and the Analytic Hierarchy Process Technique in Quality Function Deployment
,”
Total Qual. Manage.
,
9
(
6
), pp.
421
430
.
206.
Matzler
,
K.
, and
Hinterhuber
,
H. H.
,
1998
, “
How to Make Product Development Projects More Successful by Integrating Kano's Model of Customer Satisfaction Into Quality Function Deployment
,”
Technovation
,
18
(
1
), pp.
25
38
.
207.
Shen
,
X. X.
,
Tan
,
K. C.
, and
Xie
,
M.
,
2000
, “
An Integrated Approach to Innovative Product Development Using Kano's Model and QFD
,”
Eur. J. Innovation Manage.
,
3
(
2
), pp.
91
99
.
208.
Lai
,
X.
,
Tan
,
K.-C.
, and
Xie
,
M.
,
2007
, “
Optimizing Product Design Using Quantitative Quality Function Deployment: A Case Study
,”
Qual. Reliab. Eng. Int.
,
23
(
1
), pp.
45
57
.
209.
Mu
,
L.-F.
,
Tang
,
J.-F.
,
Chen
,
Y.-Z.
, and
Kwong
,
C.-K.
,
2008
, “
A Fuzzy Multi-Objective Model of QFD Product Planning Integrating Kano Model
,”
Int. J. Uncertainty, Fuzziness Knowl.-Based Syst.
,
16
(
6
), pp.
793
813
.
210.
Kwong
,
C. K.
,
Wong
,
T. C.
, and
Chan
,
K. Y.
,
2009
, “
A Methodology of Generating Customer Satisfaction Models for New Product Development Using a Neuro-Fuzzy Approach
,”
Expert Syst. Appl.
,
36
(
8
), pp.
11262
11270
.
211.
Chaudha
,
A.
,
Jain
,
R.
,
Singh
,
A.
, and
Mishra
,
P.
,
2011
, “
Integration of Kano's Model Into Quality Function Deployment (QFD)
,”
Int. J. Adv. Manuf. Technol.
,
53
(
5–8
), pp.
689
698
.
212.
Ji
,
P.
,
Jin
,
J.
,
Wang
,
T.
, and
Chen
,
Y.
,
2014
, “
Quantification and Integration of Kano's Model Into QFD for Optimising Product Design
,”
Int. J. Prod. Res.
,
52
(
21
), pp.
6335
6348
.
213.
Yadav
,
O. P.
, and
Goel
,
P. S.
,
2008
, “
Customer Satisfaction Driven Quality Improvement Target Planning for Product Development in Automotive Industry
,”
Int. J. Prod. Econ.
,
113
(
2
), pp.
997
1011
.
214.
Chen
,
C.-C.
, and
Chuang
,
M.-C.
,
2008
, “
Integrating the Kano Model Into a Robust Design Approach to Enhance Customer Satisfaction With Product Design
,”
Int. J. Prod. Econ.
,
114
(
2
), pp.
667
681
.
215.
Lin
,
S.-P.
,
Yang
,
C.-L.
,
Chan
,
Y.-h.
, and
Sheu
,
C.
,
2010
, “
Refining Kano's ‘Quality Attributes-Satisfaction’ Model: A Moderated Regression Approach
,”
Int. J. Prod. Econ.
,
126
(
2
), pp.
255
263
.
216.
Wang
,
T.
, and
Ji
,
P.
,
2010
, “
Understanding Customer Needs Through Quantitative Analysis of Kano's Model
,”
Int. J. Qual. Reliab. Manage.
,
27
(
2
), pp.
173
184
.
217.
Zhan
,
J.
,
Loh
,
H. T.
, and
Liu
,
Y.
,
2009
, “
Gather Customer Concerns From Online Product Reviews—A Text Summarization Approach
,”
Expert Syst. Appl.
,
36
(
2
), pp.
2107
2115
.
218.
Lim
,
S.
, and
Tucker
,
C. S.
,
2016
, “
A Bayesian Sampling Method for Product Feature Extraction From Large-Scale Textual Data
,”
ASME J. Mech. Des.
,
138
(
6
), p.
061403
.
219.
Tuarob
,
S.
, and
Tucker
,
C. S.
,
2015
, “
Quantifying Product Favorability and Extracting Notable Product Features Using Large Scale Social Media Data
,”
ASME J. Comput. Inf. Sci. Eng.
,
15
(
3
), p.
031003
.
220.
Wong
,
T.-L.
,
Lam
,
W.
, and
Wong
,
T.-S.
,
2008
, “
An Unsupervised Framework for Extracting and Normalizing Product Attributes From Multiple Web Sites
,”
31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
(
SIGIR'08
), Singapore, July 20–24, pp.
35
42
https://dl.acm.org/citation.cfm?id=1390343.
221.
Na
,
J.-C.
,
Thura Thet
,
T.
, and
Khoo
,
C. S. G.
,
2010
, “
Comparing Sentiment Expression in Movie Reviews From Four Online Genres
,”
Online Inf. Rev.
,
34
(
2
), pp.
317
338
.
222.
Leng
,
J.
, and
Jiang
,
P.
,
2016
, “
A Deep Learning Approach for Relationship Extraction From Interaction Context in Social Manufacturing Paradigm
,”
Knowl.-Based Syst.
,
100
, pp.
188
199
.
223.
Zhu
,
X.
,
Ming
,
Z.-Y.
,
Zhu
,
X.
, and
Chua
,
T.-S.
,
2013
, “
Topic Hierarchy Construction for the Organization of Multi-Source User Generated Contents
,”
SIGIR'13
, Dublin, Ireland, July 28–Aug. 1, pp.
233
242
https://dl.acm.org/citation.cfm?id=2484032.
224.
Jin
,
J.
,
Ji
,
P.
, and
Liu
,
Y.
,
2014
, “
Prioritising Engineering Characteristics Based on Customer Online Reviews for Quality Function Deployment
,”
J. Eng. Des.
,
25
(
7–9
), pp.
303
324
.
225.
Wang
,
P.
,
Guo
,
J.
,
Lan
,
Y.
,
Xu
,
J.
, and
Cheng
,
X.
,
2016
, “
Your Cart Tells You: Inferring Demographic Attributes From Purchase Data
,”
WSDM'16
, San Francisco, CA, Feb. 22–25, pp.
173
182
https://dl.acm.org/citation.cfm?id=2835783.
226.
Kang
,
J.
, and
Lee
,
H.
,
2017
, “
Modeling User Interest in Social Media Using News Media and Wikipedia
,”
Inf. Syst.
,
65
, pp.
52
64
.
227.
Oentaryo
,
R. J.
,
Lim
,
E.-P.
,
Chua
,
F. C. T.
,
Low
,
J.-W.
, and
Lo
,
D.
,
2016
, “
Collective Semi-Supervised Learning for User Profiling in Social Media
,” https://arxiv.org/abs/1606.07707.
228.
Si
,
J.
,
Li
,
Q.
,
Qian
,
T.
, and
Deng
,
X.
,
2013
, “
Users' Interest Grouping From Online Reviews Based on Topic Frequency and Order
,” World Wide Web, pp.
1
22
.
229.
Miao
,
Q.
,
Zhang
,
S.
,
Meng
,
Y.
, and
Yu
,
H.
,
2013
, “
Domain-Sensitive Opinion Leader Mining From Online Review Communities
,”
WWW'13 Companion
, Rio de Janeiro, Brazil, May 13–17, pp.
187
188
https://dl.acm.org/citation.cfm?id=2487788.2487882.
230.
Tang
,
J.
,
Sun
,
J.
,
Wang
,
C.
, and
Yang
,
Z.
,
2009
, “S
ocial Influence Analysis in Large-Scale Networks
,”
15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
(
KDD'09
), Paris, France, July 1, pp.
807
816
https://dl.acm.org/citation.cfm?id=1557108.
231.
Kim
,
Y. A.
, and
Srivastava
,
J.
,
2007
, “
Impact of Social Influence in E-Commerce Decision Making
,”
Ninth International Conference on Electronic Commerce
(
ICEC'07
), Minneapolis, MN, Aug. 19–22, pp.
293
302
https://dl.acm.org/citation.cfm?id=1282157.
232.
Huang
,
J.
,
Cheng
,
X.-Q.
,
Shen
,
H.-W.
,
Zhou
,
T.
, and
Jin
,
X.
,
2012
, “
Exploring Social Influence Via Posterior Effect of Word-of-Mouth Recommendations
,”
Fifth ACM International Conference on Web Search and Data Mining
(
WSDM'12
), Seattle, WA, Feb. 8–12, pp.
573
582
https://dl.acm.org/citation.cfm?id=2124365
233.
Shriver
,
S. K
,
Nair
,
H. S.
, and
Hofstetter
,
R.
,
2013
, “
Social Ties and User-Generated Content: Evidence from an Online Social Network
,”
Management Sci.
, 59(6), pp. 1425–1443.
234.
Iyengar
,
R.
, and
Van den Bulte
,
C.
,
2011
, “
Opinion Leadership and Social Contagion in New Product Diffusion
,”
Marketing Sci.
,
30
(
2
), pp.
195
212
.
235.
Kim
,
K.-J.
,
Moskowitz
,
H.
,
Dhingra
,
A.
, and
Evans
,
G.
,
2000
, “
Fuzzy Multicriteria Models for Quality Function Deployment
,”
Eur. J. Oper. Res.
,
121
(
3
), pp.
504
518
.
236.
Harding
,
J. A.
,
Popplewell
,
K.
,
Fung
, and
Omar
,
A. R.
,
2001
, “
An Intelligent Information Framework for Market Driven Product Design
,”
Comput. Ind.
,
44
(
1
), pp.
49
63
.
237.
Fung
,
R. Y. K.
,
Chen
,
Y.
, and
Tang
,
J.
,
2006
, “
Estimating the Functional Relationships for Quality Function Deployment Under Uncertainties
,”
Fuzzy Sets Syst.
,
157
(
1
), pp.
98
120
.
238.
Zhai
,
L.-Y.
,
Khoo
,
L.-P.
, and
Zhong
,
Z.-W.
,
2009
, “
A Rough Set Based Decision Support Approach to Improving Consumer Affective Satisfaction in Product Design
,”
Int. J. Ind. Ergonom.
,
39
(
2
), pp.
295
302
.
239.
Kwong
,
C. K.
,
Chen
,
Y.
,
Bai
,
H.
, and
Chan
,
D. S. K.
,
2007
, “
A Methodology of Determining Aggregated Importance of Engineering Characteristics in QFD for New Product Design
,”
Comput. Ind. Eng.
,
53
(
4
), pp.
667
679
.
240.
Chan
,
K. Y.
,
Kwong
,
C. K.
,
Dillon
,
T. S.
, and
Fung
,
K. Y.
,
2011
, “
An Intelligent Fuzzy Regression Approach for Affective Product Design That Captures Nonlinearity and Fuzziness
,”
J. Eng. Des.
,
22
(
8
), pp.
523
542
.
241.
Fung
,
K. Y.
,
Kwong
,
C. K.
,
Siu
,
K. W. M.
, and
Yu
,
K. M.
,
2012
, “
A Multi-Objective Genetic Algorithm Approach to Rule Mining for Affective Product Design
,”
Expert Syst. Appl.
,
39
(
8
), pp.
7411
7419
.
242.
(Roger) Jiao
,
J.
,
Yiyang
,
Z.
, and
Helander
,
M.
,
2006
, “
A Kansei Mining System for Affective Design
,”
Expert Syst. Appl.
,
30
(
4
), pp.
658
673
.
243.
Shen
,
H.-C.
, and
Wang
,
K.-C.
,
2016
, “
Affective Product Form Design Using Fuzzy Kansei Engineering and Creativity
,”
J. Ambient Intell. Humanized Comput.
,
7
(
6
), pp.
875
888
.
244.
Jin
,
J.
,
Ji
,
P.
,
Liu
,
Y.
, and
Johnson Lim
,
S. C.
,
2015
, “
Translating Online Customer Opinions Into Engineering Characteristics in QFD: A Probabilistic Language Analysis Approach
,”
Eng. Appl. Artif. Intell.
,
41
, pp.
115
127
.
245.
Zhou
,
F.
,
Jiao
,
J. R.
,
Schaefer
,
D.
, and
Chen
,
S.
, “
Hybrid Association Mining and Refinement for Affective Mapping in Emotional Design
,”
ASME J. Comput. Inf. Sci. Eng.
,
10
(
3
), p.
031010
.
246.
Chen
,
Y.
,
Fung
,
R. Y. K.
, and
Tang
,
J.
,
2006
, “
Rating Technical Attributes in Fuzzy QFD by Integrating Fuzzy Weighted Average Method and Fuzzy Expected Value Operator
,”
Eur. J. Oper. Res.
,
174
(
3
), pp.
1553
1566
.
247.
Akao
,
Y.
,
1990
,
Quality Function Deployment: Integrating Customer Requirements Into Product Design
,
Productivity Press
, Boston, MA.
248.
Wang
,
X.-T.
, and
Xiong
,
W.
,
2011
, “
An Integrated Linguistic-Based Group Decision-Making Approach for Quality Function Deployment
,”
Expert Syst. Appl.
,
38
(
12
), pp.
14428
14438
.
249.
Lan
,
L.
,
Liu
,
Y.
, and
Lu
,
W.
,
2017
, “
Automatic Discovery of Design Task Structure Using Deep Belief Nets
,”
ASME J. Comput. Inf. Sci. Eng.
,
17
(
4
), p.
041001
.
250.
Liu
,
Y.-C.
,
Chakrabarti
,
A.
, and
Bligh
,
T.
,
2003
, “
Towards an ‘Ideal’ Approach for Concept Generation
,”
Des. Stud.
,
24
(
4
), pp.
341
355
.
251.
Nagai
,
Y.
,
Taura
,
T.
, and
Mukai
,
F.
,
2009
, “
Concept Blending and Dissimilarity: Factors for Creative Concept Generation Process
,”
Des. Stud.
,
30
(
6
), pp.
648
675
.
252.
Liu
,
A.
, and
Lu
,
S. C.-Y.
,
2014
, “
Alternation of Analysis and Synthesis for Concept Generation
,”
CIRP Ann.
,
63
(
1
), pp.
177
180
.
253.
Rondini
,
A.
,
Pezzotta
,
G.
,
Pirola
,
F.
,
Rossi
,
M.
, and
Pina
,
P.
,
2016
, “
How to Design and Evaluate Early Pss Concepts: The Product Service Concept Tree
,”
Procedia CIRP
,
50
(
Suppl. C
), pp.
366
371
.
254.
Di Gironimo
,
G.
,
Carfora
,
D.
,
Esposito
,
G.
,
Labate
,
C.
,
Mozzillo
,
R.
,
Renno
,
F.
,
Lanzotti
,
A.
, and
Siuko
,
M.
,
2013
, “
Improving Concept Design of Divertor Support System for Fast Tokamak Using Triz Theory and Ahp Approach
,”
Fusion Eng. Des.
,
88
(
11
), pp.
3014
3020
.
255.
Hilmann
,
J.
,
Paas
,
M.
,
Haenschke
,
A.
, and
Vietor
,
T.
,
2007
, “
Automatic Concept Model Generation for Optimisation and Robust Design of Passenger Cars
,”
Adv. Eng. Software
,
38
(
11–12
), pp.
795
801
.
256.
Tiwari
,
V.
,
Jain
,
P. K.
, and
Tandon
,
P.
,
2016
, “
Product Design Concept Evaluation Using Rough Sets and Vikor Method
,”
Adv. Eng. Inf.
,
30
(
1
), pp.
16
25
.
257.
Yan
,
W.
,
Chen
,
C.-H.
, and
Shieh
,
M.-D.
,
2006
, “
Product Concept Generation and Selection Using Sorting Technique and Fuzzy c-Means Algorithm
,”
Comput. Ind. Eng.
,
50
(
3
), pp.
273
285
.
258.
Huang
,
H.-Z.
,
Bo
,
R.
, and
Chen
,
W.
,
2006
, “
An Integrated Computational Intelligence Approach to Product Concept Generation and Evaluation
,”
Mech. Mach. Theory
,
41
(
5
), pp.
567
583
.
259.
Chou
,
J.-R.
,
2014
, “
An Ideation Method for Generating New Product Ideas Using Triz, Concept Mapping, and Fuzzy Linguistic Evaluation Techniques
,”
Adv. Eng. Inf.
,
28
(
4
), pp.
441
454
.
260.
Kurtoglu
,
T.
,
Campbell
,
M. I.
, and
Linsey
,
J. S.
,
2009
, “
An Experimental Study on the Effects of a Computational Design Tool on Concept Generation
,”
Des. Stud.
,
30
(
6
), pp.
676
703
.
261.
Liang
,
Y.
,
Liu
,
Y.
,
Kwong
,
C. K.
, and
Lee
,
W. B.
,
2012
, “
Learning the “Whys”: Discovering Design Rationale Using Text Mining—An Algorithm Perspective
,”
Comput.-Aided Des.
,
44
(
10
), pp.
916
930
.
262.
Yamamoto
,
E.
,
Taura
,
T.
,
Ohashi
,
S.
, and
Yamamoto
,
M.
, “
A Method for Function Dividing in Conceptual Design by Focusing on Linguistic Hierarchal Relations
,”
ASME J. Comput. Inf. Sci. Eng.
,
10
(
3
), p.
031004
.
263.
Dering
,
M. L.
, and
Tucker
,
C. S.
,
2017
, “
A Convolutional Neural Network Model for Predicting a Product's Function, Given Its Form
,”
ASME J. Mech. Des.
,
139
(
11
), p.
111408
.
264.
Hao
,
J.
,
Zhao
,
Q.
, and
Yan
,
Y.
,
2017
, “
A Function-Based Computational Method for Design Concept Evaluation
,”
Adv. Eng. Inf.
,
32
(
Suppl. C
), pp.
237
247
.
265.
Cao
,
D.
,
Li
,
Z.
, and
Ramani
,
K.
,
2011
, “
Ontology-Based Customer Preference Modeling for Concept Generation
,”
Adv. Eng. Inf.
,
25
(
2
), pp.
162
176
.
266.
Park
,
Y.
, and
Lee
,
S.
,
2011
, “
How to Design and Utilize Online Customer Center to Support New Product Concept Generation
,”
Expert Syst. Appl.
,
38
(
8
), pp.
10638
10647
.
267.
Liu
,
A.
, and
Lu
,
S. C.-Y.
,
2016
, “
A Crowdsourcing Design Framework for Concept Generation
,”
CIRP Ann.
,
65
(
1
), pp.
177
180
.
268.
Wang
,
L.
,
Youn
,
B. D.
,
Azarm
,
S.
, and
Kannan
,
P. K.
,
2011
, “
Customer-Driven Product Design Selection Using Web Based User-Generated Content
,”
ASME
Paper No. DETC2011-48338.
269.
Chang
,
D.
, and
Chen
,
C.-H.
,
2015
, “
Product Concept Evaluation and Selection Using Data Mining and Domain Ontology in a Crowdsourcing Environment
,”
Adv. Eng. Inf.
,
29
(
4
), pp.
759
774
.
270.
Won
,
P.-H.
,
2001
, “
The Comparison Between Visual Thinking Using Computer and Conventional Media in the Concept Generation Stages of Design
,”
Autom. Constr.
,
10
(
3
), pp.
319
325
.
271.
Kang
,
J.
,
Kang
,
Z.
,
Qin
,
S.
,
Wang
,
H.
, and
Wright
,
D.
,
2013
, “
Instant 3D Design Concept Generation and Visualization by Real-Time Hand Gesture Recognition
,”
Comput. Ind.
,
64
(
7
), pp.
785
797
.
272.
Farnsworth
,
M.
, and
Tomiyama
,
T.
,
2014
, “
Capturing, Classification and Concept Generation for Automated Maintenance Tasks
,”
CIRP Ann.
,
63
(
1
), pp.
149
152
.
273.
Tsenn
,
J.
,
Atilola
,
O.
,
McAdams
,
D. A.
, and
Linsey
,
J. S.
,
2014
, “
The Effects of Time and Incubation on Design Concept Generation
,”
Des. Stud.
,
35
(
5
), pp.
500
526
.
274.
Dong
,
A.
,
Lovallo
,
D.
, and
Mounarath
,
R.
,
2015
, “
The Effect of Abductive Reasoning on Concept Selection Decisions
,”
Des. Stud.
,
37
, pp.
37
58
.
275.
Bracke
,
S.
,
Yamada
,
S.
,
Kinoshita
,
Y.
,
Inoue
,
M.
, and
Yamada
,
T.
,
2017
, “
Decision Making Within the Conceptual Design Phase of Eco-Friendly Products
,”
Procedia Manuf.
,
8
(
Suppl. C
), pp.
463
470
.
276.
Noguchi
,
H.
,
1997
, “
An Idea Generation Support System for Industrial Designers (Idea Sketch Processor)
,”
Knowl.-Based Syst.
,
10
(
1
), pp.
37
42
.
277.
Akasaka
,
F.
,
Nemoto
,
Y.
,
Chiba
,
R.
, and
Shimomura
,
Y.
,
2012
, “
Development of PSS Design Support System: Knowledge-Based Design Support and Qualitative Evaluation
,”
Procedia CIRP
,
3
(
Suppl. C
), pp.
239
244
.
278.
Ko
,
Y.-T.
,
2017
, “
Modeling a Hybrid-Compact Design Matrix for New Product Innovation
,”
Comput. Ind. Eng.
,
107
(
Suppl. C
), pp.
345
59
.
279.
Moon
,
H.
, and
Han
,
S. H.
,
2016
, “
A Creative Idea Generation Methodology by Future Envisioning From the User Experience Perspective
,”
Int. J. Ind. Ergonom.
,
56
(
Suppl. C
), pp.
84
96
.
280.
Jahanmir
,
S. F.
, and
Lages
,
L. F.
,
2015
, “
The Lag-User Method: Using Laggards as a Source of Innovative Ideas
,”
J. Eng. Technol. Manage.
,
37
(
Suppl. C
), pp.
65
77
.
281.
Lugt
,
R.
, and
van der
,
2005
, “
How Sketching Can Affect the Idea Generation Process in Design Group Meetings
,”
Des. Stud.
,
26
(
2
), pp.
101
122
.
282.
Hirunyawipada
,
T.
, and
Paswan
,
A. K.
,
2013
, “
Effects of Team Cognition and Constraint on New Product Ideation
,”
J. Bus. Res.
,
66
(
11
), pp.
2332
2337
.
283.
Zhang
,
C.
,
Kwon
,
Y. P.
,
Kramer
,
J.
,
Kim
,
E.
, and
Alice
,
M. A.
,
2018
, “
Deep Learning for Design in Concept Clustering
,”
IDETC'17
, pp.
68352
68312
.
284.
Zahay
,
D.
,
Hajli
,
N.
, and
Sihi
,
D.
,
2018
, “
Managerial Perspectives on Crowdsourcing in the New Product Development Process
,”
Ind. Marketing Manage.
,
71
, pp. 41–53.
285.
Simon
,
F.
, and
Tellier
,
A.
,
2011
, “
How Do Actors Shape Social Networks During the Process of New Product Development?
,”
Eur. Manage. J.
,
29
(
5
), pp.
414
430
.
286.
McAdam
,
R.
, and
McClelland
,
J.
,
2002
, “
Sources of New Product Ideas and Creativity Practices in the UK Textile Industry
,”
Technovation
,
22
(
2
), pp.
113
121
.
287.
Tseng
,
I.
,
Moss
,
J.
,
Cagan
,
J.
, and
Kotovsky
,
K.
,
2008
, “
The Role of Timing and Analogical Similarity in the Stimulation of Idea Generation in Design
,”
Des. Stud.
,
29
(
3
), pp.
203
221
.
288.
Starkey
,
E.
,
Toh
,
C. A.
, and
Miller
,
S. R.
,
2016
, “
Abandoning Creativity: The Evolution of Creative Ideas in Engineering Design Course Projects
,”
Des. Stud.
,
47
(
Suppl. C
), pp.
47
72
.
289.
Nonaka
,
I.
,
Byosiere
,
P.
,
Borucki
,
C. C.
, and
Konno
,
N.
,
1994
, “
Organizational Knowledge Creation Theory—A First Comprehensive Test
,”
Int. Bus. Rev.
,
3
(
4
), pp.
337
351
.
290.
Nonaka
,
I.
,
Toyama
,
R.
, and
Konno
,
N.
,
2000
, “
SECI Ba and Leadership: A Unified Model of Dynamic Knowledge Creation
,”
Long Range Plann.
,
33
(
1
), pp.
5
34
.
291.
Nonaka
,
I.
,
Krogh
,
G.
, and
Voelpel
,
S.
,
2006
, “
Organizational Knowledge Creation Theory: Evolutionary Paths and Future Advances
,”
Org. Stud.
,
27
(
8
), pp.
1179
1208
.
292.
Nonaka
,
I.
, and
Takeuchi
,
H.
,
1995
,
The Knowledge-Creating Company
,
Oxford University Press
,
New York
.
293.
Schulze
,
A.
, and
Hoegl
,
M.
,
2008
, “
Organizational Knowledge Creation and the Generation of New Product Ideas: A Behavioral Approach
,”
Res. Policy
,
37
(
10
), pp.
1742
1750
.
294.
Gomes
,
P.
,
Seco
,
N.
,
Pereira
,
F. C.
,
Paulo
,
P.
,
Paulo
,
C.
,
Ferreira
,
J. L.
, and
Bento
,
C.
,
2006
, “
The Importance of Retrieval in Creative Design Analogies
,”
Knowl.-Based Syst.
,
19
(
7
), pp.
480
488
.
295.
Jauregui-Becker
,
J. M.
, and
Wits
,
W. W.
,
2012
, “
Knowledge Structuring and Simulation Modeling for Product Development
,”
Procedia CIRP
,
2
(
Suppl. C
), pp.
4
9
.
296.
Verhaegen
,
P.-A.
,
Vandevenne
,
D.
,
Peeters
,
J.
, and
Duflou
,
J. R.
,
2013
, “
Refinements to the Variety Metric for Idea Evaluation
,”
Des. Stud.
,
34
(
2
), pp.
243
263
.
297.
Atilola
,
O.
,
Tomko
,
M.
, and
Linsey
,
J. S.
,
2016
, “
The Effects of Representation on Idea Generation and Design Fixation: A Study Comparing Sketches and Function Trees
,”
Des. Stud.
,
42
(
Suppl. C
), pp.
110
136
.
298.
Nasr
,
S. B.
,
Becan
,
G.
,
Acher
,
M.
,
Filho
,
J. B. F.
,
Sannier
,
N.
,
Baudry
,
B.
, and
Davril
,
J.-M.
,
2017
, “
Automated Extraction of Product Comparison Matrices From Informal Product Descriptions
,”
J. Syst. Software
,
124
(
Suppl. C
), pp.
82
103
.
299.
Lim
,
S. C.
,
Johnson
,
Y.
,
Liu
,
W. B.
, and
Lee
,
2010
, “
Multi-Facet Product Information Search and Retrieval Using Semantically Annotated Product Family Ontology
,”
Inf. Process. Manage.
,
46
(
4
), pp.
479
493
.
300.
Petiot
,
J.-F.
, and
Yannou
,
B.
,
2004
, “
Measuring Consumer Perceptions for a Better Comprehension, Specification and Assessment of Product Semantics
,”
Int. J. Ind. Ergonom.
,
33
(
6
), pp.
507
525
.
301.
Shi
,
F.
,
Chen
,
L.
,
Han
,
J.
, and
Childs
,
P.
,
2017
, “
A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval
,”
ASME J. Mech. Des.
,
139
(
11
), p.
111402
.
302.
Huang
,
Y.
,
Chen
,
C.-H.
,
Wang
,
I.-H. C.
, and
Khoo
,
L. P.
,
2014
, “
A Product Configuration Analysis Method for Emotional Design Using a Personal Construct Theory
,”
Int. J. Ind. Ergonom.
,
44
(
1
), pp.
120
130
.
303.
Vladimir
,
H.
,
Sokol
,
O.
, and
Cerny
,
M.
,
2017
, “
Clustering Retail Products Based on Customer Behaviour
,”
Appl. Soft Comput.
,
60
, pp.
752
762
.
304.
Bracke
,
S.
, and
Rosebrock
,
C.
,
2016
, “
Contribution for Product Analyses to Quantify and Predict Similar or Diverse Eco-Related Product Perception in the Usage Phase
,”
Procedia CIRP
,
40
(
Suppl. C
), pp.
68
72
.
305.
Li
,
Y.-L.
,
Tang
,
J.-F.
,
Chin
,
K.-S.
,
Luo
,
X.-G.
,
Pu
,
Y.
, and
Jiang
,
Y.-S.
,
2012
, “
On Integrating Multiple Type Preferences Into Competitive Analyses of Customer Requirements in Product Planning
,”
Int. J. Prod. Econ.
,
139
(
1
), pp.
168
179
.
306.
Yuen
,
K. K. F.
,
2017
, “
The Fuzzy Cognitive Pairwise Comparisons for Ranking and Grade Clustering to Build a Recommender System: An Application of Smartphone Recommendation
,”
Eng. Appl. Artif. Intell.
,
61
(
Suppl. C
), pp.
136
151
.
307.
Yuen
,
K.
, and
Fung
,
K.
,
2013
, “
Toward a Ranking Strategy for E-Commerce Products in an E-Alliance Portal Using Primitive Cognitive Network Process
,”
Procedia Comput. Sci.
,
17
(
Suppl. C
), pp.
1091
1096
.
308.
Netzer
,
O.
,
Feldman
,
R.
,
Goldenberg
,
J.
, and
Fresko
,
M.
,
2012
, “
Mine Your Own Business: Market-Structure Surveillance Through Text Mining
,”
Marketing Sci.
,
31
(
3
), pp.
521
543
.
309.
Li
,
S.
,
Zha
,
Z.-J.
,
Ming
,
Z.
,
Wang
,
M.
,
Chua
,
T.-S.
,
Guo
,
J.
, and
Xu
,
W.
,
2011
, “
Product Comparison Using Comparative Relations
,”
SIGIR'11
, Beijing, China, pp.
1151
1152
.
310.
Zhang
,
Z.
,
Guo
,
C.
, and
Goes
,
P.
,
2013
, “
Product Comparison Networks for Competitive Analysis of Online Word-of-Mouth
,”
ACM Trans. Manage. Inf. Syst.
,
3
(
4
), pp.
1
20
.
311.
Chen
,
K.
,
Luo
,
P.
, and
Wang
,
H.
,
2017
, “
Investigating Transitive Influences on Wom: From the Product Network Perspective
,”
Electron. Commerce Res.
,
17
(
1
), pp.
149
167
.
312.
Zhang
,
W.
,
Xu
,
H.
, and
Wan
,
W.
,
2012
, “
Weakness Finder: Find Product Weakness From Chinese Reviews by Using Aspects Based Sentiment Analysis
,”
Expert Syst. Appl.
,
39
(
11
), pp.
10283
10291
.
313.
Liu
,
Y.
,
Bi
,
J.-W.
, and
Fan
,
Z.-P.
,
2017
, “
Ranking Products Through Online Reviews: A Method Based on Sentiment Analysis Technique and Intuitionistic Fuzzy Set Theory
,”
Inf. Fusion
,
36
, pp.
149
161
.
314.
Li
,
H.
,
Bhowmick
,
S. S.
, and
Sun
,
A.
,
2010
, “
Affinity-Driven Prediction and Ranking of Products in Online Product Review Sites
,”
CIKM'10
, Toronto, ON, Canada, pp.
1745
1748
.
315.
Yang
,
X.
,
Yang
,
G.
, and
Wu
,
J.
,
2016
, “
Integrating Rich and Heterogeneous Information to Design a Ranking System for Multiple Products
,”
Decis. Support Syst.
,
84
(
Supp. C
), pp.
117
133
.
316.
Flint
,
D. J.
,
2002
, “
Compressing New Product Success-to-Success Cycle Time: Deep Customer Value Understanding and Idea Generation
,”
Ind. Marketing Manage.
,
31
(
4
), pp.
305
315
.
317.
Wei
,
W.
,
Ji
,
J.
,
Wuest
,
T.
, and
Tao
,
F.
,
2017
, “
Product Family Flexible Design Method Based on Dynamic Requirements Uncertainty Analysis
,”
Procedia CIRP
,
60
, pp.
332
337
.
318.
Chen
,
S. L.
,
Jiao
,
R. J.
, and
Tseng
,
M. M.
,
2009
, “
Evolutionary Product Line Design Balancing Customer Needs and Product Commonality
,”
CIRP Ann.
,
58
(
1
), pp.
123
126
.
319.
Cichos
,
D.
, and
Aurich
,
J. C.
,
2016
, “
Support of Engineering Changes in Manufacturing Systems by Production Planning and Control Methods
,”
Procedia CIRP
,
41
, pp.
165
170
.
320.
Wang
,
H. S.
,
Che
,
Z. H.
, and
Wang
,
M. J.
,
2009
, “
A Three-Phase Integrated Model for Product Configuration Change Problems
,”
Expert Syst. Appl.
,
36
(
3
), pp.
5491
5509
.
321.
Li
,
Y.
,
Zhao
,
W.
, and
Shao
,
X.
,
2012
, “
A Process Simulation Based Method for Scheduling Product Design Change Propagation
,”
Adv. Eng. Inf.
,
26
(
3
), pp.
529
538
.
322.
Azadeh
,
A.
,
Sadri
,
S.
,
Saberi
,
M.
,
Yoon
,
J. H.
,
Chang
,
E.
,
Khadeer Hussain
,
O.
, and
Pourmohammad Zia
,
N.
,
2017
, “
An Integrated Fuzzy Trust Prediction Approach in Product Design and Engineering
,”
Int. J. Fuzzy Syst.
,
19
(
4
), pp.
1190
1199
.
323.
Chong
,
Y. T.
, and
Chen
,
C.-H.
,
2010
, “
Management and Forecast of Dynamic Customer Needs: An Artificial Immune and Neural System Approach
,”
Adv. Eng. Inf.
,
24
(
1
), pp.
96
106
.
324.
Tucker
,
C. S.
, and
Kim
,
H. M.
,
2011
, “
Trend Mining for Predictive Product Design
,”
ASME J. Mech. Des.
,
133
(
11
), p.
111008
.
325.
Ma
,
J.
,
Kwak
,
M.
, and
Kim
,
H. M.
,
2014
, “
Demand Trend Mining for Predictive Life Cycle Design
,”
J. Cleaner Prod.
,
68
, pp.
189
199
.
326.
Guo
,
J.
,
Tan
,
R.
,
Sun
,
J.
,
Ren
,
J.
,
Wu
,
S.
, and
Qiu
,
Y.
,
2016
, “
A Needs Analysis Approach to Product Innovation Driven by Design
,”
Procedia CIRP
,
39
(
Suppl. C
), pp.
39
44
.
327.
Tucker
,
C.
, and
Kim
,
H. M.
,
2011
, “
Predicting Emerging Product Design Trend by Mining Publicity Available Customer Review Data
,”
ICED'11
, Copenhagen, Denmark, pp.
43
52
.
328.
Goorha
,
S.
, and
Ungar
,
L.
,
2010
, “
Discovery of Significant Emerging Trends
,”
16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
(
KDD'10
), Washington, DC, July 25–28, pp.
57
64
https://dl.acm.org/citation.cfm?id=1835804.1835815.
329.
Malone
,
T. W.
,
2014
, “
A Revolution in Business
,”
MIT Technology Review
, epub, accessed Aug. 23, 2018, https://www.technologyreview.com/s/526136/a-revolution-in-business/
330.
Schiller
,
D.
,
2015
, “
The Internet and Business
,”
MIT Technology Review
, epub, accessed Aug. 23, 2018, https://www.technologyreview.com/s/535076/the-internet-and-business/
331.
Evans
,
P.
,
2015
, “
From Deconstruction to Big Data: How Technology Is Reshaping the Corporation
,”
MIT Technology Review
, epub, accessed Aug. 23, 2018, https://www.technologyreview.com/s/537461/from-deconstruction-to-big-data-how-technology-is-reshaping-the-corporation/
You do not currently have access to this content.