Abstract

Eliciting user needs for individual components and features of a product or a service on a large scale is a key requirement for innovative design. Synthesizing data as an initial discovery phase of a design process is usually accomplished with a small number of participants, employing qualitative research methods such as observations, focus groups, and interviews. This leaves an entire swath of pertinent user behavior, preferences, and opinions not captured. Sentiment analysis is a key enabler for large-scale need finding from online user reviews generated on a regular basis. A major limitation of current sentiment analysis approaches used in design sciences, however, is the need for laborious labeling and annotation of large review datasets for training, which in turn hinders their scalability and transferability across different domains. This article proposes an efficient and scalable methodology for automated and large-scale elicitation of attribute-level user needs. The methodology builds on the state-of-the-art pretrained deep language model, BERT (Bidirectional Encoder Representations from Transformers), with new convolutional net and named entity recognition (NER) layers for extracting attribute, description, and sentiment words from online user review corpora. The machine translation algorithm BLEU (BiLingual Evaluation Understudy) is utilized to extract need expressions in the form of predefined part-of-speech combinations (e.g., adjective–noun, verb–noun). Numerical experiments are conducted on a large dataset scraped from a major e-commerce retail store for apparel and footwear to demonstrate the performance, feasibility, and potentials of the developed methodology.

References

References
1.
Laurie Fullerton
,
2017
, “
Online Reviews Impact Purchasing Decisions for Over 93% of Consumers
,”The Drum, https://www.thedrum.com/news/2017/03/27/online-reviews-impact-purchasing-decisions-over-93-consumers-report-suggests, Accessed 5 November 2020.
2.
Rosie Murphy
,
2019
, “
Local Consumer Review Survey: How Customers Use Online Reviews'
,” https://www.brightlocal.com/research/local-consumer-review-survey/, Accessed 5 November 2020.
3.
Cooper
,
R. G.
,
Edgett
,
S. J.
, and
Kleinschmidt
,
E. J.
,
2004
, “
Benchmarking Best NPD Practices—III
,”
Research Technology Management
, pp.
43
55
.
4.
Osborn
,
A. F.
,
1953
,
Applied Imagination
,
Scribner’s
,
New York
.
5.
Marion
,
T. J.
, and
Fixson
,
S. K.
,
2018
,
The Innovation Navigator: Transforming Your Organization in the Era of Digital Design and Collaborative Culture
,
University of Toronto Press
,
Toronto, Canada
.
6.
Radford
,
A.
,
Narasimhan
,
K.
,
Salimans
,
T.
, and
Sutskever
,
I.
,
2018
, “
27th International Conference on Computational Linguistics
,”
ACL 2018
,
Santa Fe, NM
,
Aug. 7
.
7.
Devlin
,
J.
,
Chang
,
M.-W.
,
Lee
,
K.
, and
Toutanova
,
K.
,
2018
,
Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
,”
NAACL
,
Minneapolis, MN
,
June
.
8.
Nadeau
,
D.
, and
Sekine
,
S.
,
2007
, “
A Survey of Named Entity Recognition and Classification
,”
Lingvisticæ Investigationes
,
30
(
1
), pp.
1
20
. 10.1075/li.30.1.01for
9.
Li
,
X.
,
Bing
,
L.
,
Li
,
P.
,
Lam
,
W.
, and
Yang
,
Z.
,
2018
, “
IJCAI-ECAI-18
,”
27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence
,
Stockholm, Sweden
,
July 13–19
, pp.
4194
4200
.
10.
Yadav
,
V.
, and
Bethard
,
S.
,
2018
, “
27th International Conference on Computational Linguistics
,”
27th International Conference on Computational Linguistics
,
Santa Fe, NM
,
July 16–18
.
11.
Schaffhausen
,
C. R.
, and
Kowalewski
,
T. M.
,
2015
, “
Large-Scale Needfinding: Methods of Increasing User-Generated Needs From Large Populations
,”
ASME J. Mech. Des.
,
137
(
7
), p.
071403
. 10.1115/1.4030161
12.
Mcfadzean
,
S.
, and
Sketch
,
J.
,
1997
, “
Managing Effective Communication in Knitwear Design
,”
Design J.
, pp.
21
42
.
13.
Rasoulifar
,
G.
,
Eckert
,
C.
, and
Prudhomme
,
G.
,
2015
, “
Communicating Consumer Needs in the Design Process of Branded Products
,”
ASME J. Mech. Design.
,
137
(
7
), p.
071404
. 10.1115/1.4030050
14.
Griffin
,
A.
, and
Hauser
,
J. R.
,
1993
, “
The Voice of the Customer
,”
Mark. Sci.
,
12
(
1
), pp.
1
27
. 10.1287/mksc.12.1.1
15.
Fogliatto
,
F. S.
, and
da Silveira
,
G. J. C.
,
2008
, “
Mass Customization: A Method for Market Segmentation and Choice Menu Design
,”
Int. J. Prod. Econ.
,
111
(
2
), pp.
606
622
. 10.1016/j.ijpe.2007.02.034
16.
Franke
,
N.
,
Schreier
,
M.
, and
Kaiser
,
U.
,
2010
, “
The “I Designed It Myself” Effect in Mass Customization
,”
Manage. Sci.
,
56
(
1
), pp.
125
140
. 10.1287/mnsc.1090.1077
17.
Felfernig
,
A.
,
2007
, “
Standardized Configuration Knowledge Representations as Technological Foundation for Mass Customization
,”
IEEE Trans. Eng. Manage.
,
54
(
1
), pp.
41
56
. 10.1109/TEM.2006.889066
18.
Franke
,
N.
,
Keinz
,
P.
, and
Steger
,
C. J.
,
Sep 2009
, “
Testing the Value of Customization: When Do Customers Really Prefer Products Tailored to Their Preferences?
,”
J. Mark.
,
73
(
5
), pp.
103
121
. 10.1509/jmkg.73.5.103
19.
Lord
,
C. G.
,
Ross
,
L.
, and
Lepper
,
M. R.
,
1979
, “
Biased Assimilation and Attitude Polarization: The Effects of Prior Theories on Subsequently Considered Evidence
,”
J. Personal. Soc. Psychology
,
37
(
11
), pp.
2098
2109
. 10.1037/0022-3514.37.11.2098
20.
Fogliatto
,
F. S.
,
Da Silveira
,
G. J. C.
, and
Borenstein
,
D.
,
2012
, “
The Mass Customization Decade: An Updated Review of the Literature
,”
Int. J. Prod. Econ.
,
138
(
1
), pp.
14
25
. 10.1016/j.ijpe.2012.03.002
21.
Lee
,
T. Y.
, and
Bradlow
,
E. T.
,
2011
, “
Automated Marketing Research Using Online Customer Reviews
,”
J. Mark. Res.
,
48
(
5
), pp.
881
894
. 10.1509/jmkr.48.5.881
22.
Pang
,
B.
, and
Lee
,
L.
,
2006
, “
Opinion Mining and Sentiment Analysis
,”
Found. Trends Inform. Retrieval
,
1
(
2
), pp.
91
231
. 10.1561/1500000001
23.
Ravi
,
K.
, and
Ravi
,
V.
,
2015
, “
A Survey on Opinion Mining and Sentiment Analysis: Tasks, Approaches and Applications
,”
Knowledge Based Syst.
,
89
(
5
), pp.
14
46
. 10.1016/j.knosys.2015.06.015
24.
Tang
,
H.
,
Tan
,
S.
, and
Cheng
,
X.
,
2009
, “
A Survey on Sentiment Detection of Reviews
,”
Expert Syst. Appl.
,
36
(
7
), pp.
10760
10773
. 10.1016/j.eswa.2009.02.063
25.
Zhang
,
L.
,
Wang
,
S.
, and
Liu
,
B.
,
2018
, “
Deep Learning for Sentiment Analysis : {A} Survey
,”
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
,
7
, pp.
12
35
.
26.
Hoyle
,
C.
,
Chen
,
W.
,
Wang
,
N.
, and
Koppelman
,
F. S.
,
2010
, “
Integrated Bayesian Hierarchical Choice Modeling to Capture Heterogeneous Consumer Preferences in Engineering Design
,”
ASME J. Mech. Des.
,
132
(
12
), p.
121010
. 10.1115/1.4002972
27.
Thelwall
,
M.
,
Buckley
,
K.
,
Paltoglou
,
G.
,
Cai
,
D.
, and
Kappas
,
A.
,
Dec 2010
, “
Sentiment Strength Detection in Short Informal Text
,”
J. Am. Soc. Inform. Sci. Technol.
,
61
(
12
), pp.
2544
2558
. 10.1002/asi.21416
28.
El Dehaibi
,
N.
,
Goodman
,
N. D.
, and
MacDonald
,
E. F.
,
2019
, “
Extracting Customer Perceptions of Product Sustainability From Online Reviews
,”
ASME J. Mech. Des.
,
141
(
12
), p.
121103
. 10.1115/1.4044522
29.
He
,
Y.
,
Camburn
,
B.
,
Liu
,
H.
,
Luo
,
J.
,
Yang
,
M.
, and
Wood
,
K.
,
2019
, “
Mining and Representing the Concept Space of Existing Ideas for Directed Ideation
,”
ASME J. Mech. Des.
,
141
(
12
), p.
121101
. 10.1115/1.4044399
30.
Bing
,
L.
,
2015
,
Sentiment Analysis: Mining Opinions, Sentiments, and Emotions
, Vol.
38
,
The Cambridge University Press
,
Cambridge, UK
, pp.
41
51
.
31.
Ireland
,
R.
, and
Liu
,
A.
,
2018
, “
Application of Data Analytics for Product Design: Sentiment Analysis of Online Product Reviews
,”
CIRP. J. Manuf. Sci. Technol.
,
23
(
7
), pp.
128
144
. 10.1016/j.cirpj.2018.06.003
32.
Decker
,
R.
, and
Trusov
,
M.
,
2010
, “
Estimating Aggregate Consumer Preferences From Online Product Reviews
,”
Int. J. Res. Market.
,
27
(
4
), pp.
293
307
. 10.1016/j.ijresmar.2010.09.001
33.
Rai
,
R.
,
2012
, “
Identifying Key Product Attributes and Their Importance Levels From Online Customer Reviews
,”
Proceedings of the ASME Design Engineering Technical Conference
,
Chicago, IL
,
Aug. 8–12
, Vol.
3
, pp.
533
540
.
34.
Stone
,
T.
, and
Choi
,
S. K.
,
2013
, “
Extracting Consumer Preference From User-Generated Content Sources Using Classification
,”
SME 2013 International Design Engineering Technical Conferences (IDETC) and Computers and Information in Engineering Conference (CIE)
,
Portland, OR
,
Aug. 4–7
.
35.
Qi
,
J.
,
Zhang
,
Z.
,
Jeon
,
S.
, and
Zhou
,
Y.
,
2016
, “
Mining Customer Requirements From Online Reviews: A Product Improvement Perspective
,”
Inform. Manage.
,
53
(
8
), pp.
951
963
. 10.1016/j.im.2016.06.002
36.
Lim
,
S.
, and
Tucker
,
C. S.
,
2017
, “
Mitigating Online Product Rating Biases Through the Discovery of Optimistic, Pessimistic, and Realistic Reviewers
,”
ASME J. Mech. Des.
,
139
(
11
), p.
111409
. 10.1115/1.4037612
37.
Wang
,
Y.
,
Mo
,
D. Y.
, and
Tseng
,
M. M.
,
2018
, “
Mapping Customer Needs to Design Parameters in the Front End of Product Design by Applying Deep Learning
,”
CIRP. Ann.
,
67
(
1
), pp.
145
148
. 10.1016/j.cirp.2018.04.018
38.
Suryadi
,
D.
, and
Kim
,
H. M.
,
2019
, “
A Data-Driven Methodology to Construct Customer Choice Sets Using Online Data and Customer Reviews
,”
ASME J. Mech. Des.
,
141
(
11
), p.
111103
. 10.1115/1.4044198
39.
Zhou
,
F.
,
Jiao
,
R. J.
, and
Linsey
,
J. S.
,
2015
, “
Latent Customer Needs Elicitation by Use Case Analogical Reasoning From Sentiment Analysis of Online Product Reviews
,”
ASME J. Mech. Des.
,
137
(
7
), p.
071401
. 10.1115/1.4030159
40.
Zeng
,
J.
,
Liu
,
Y.
,
Su
,
J.
,
Ge
,
Y.
,
Lu
,
Y.
,
Yin
,
Y.
, and
Luo
,
J.
,
2019
, “
Iterative Dual Domain Adaptation for Neural Machine Translation
,”
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
,
Hong Kong
,
Nov. 3–7
, pp.
845
855
.
41.
Wang
,
D. s.
,
Liu
,
J. z.
,
Zhu
,
A. x.
,
Wang
,
S.
,
Zeng
,
C. y.
, and
Ma
,
T. w.
,
2019
, “
Automatic Extraction and Structuration of Soil–Environment Relationship Information From Soil Survey Reports
,”
J. Integrat. Agricult.
,
18
(
2
), pp.
328
339
. 10.1016/S2095-3119(18)62071-4
42.
Yang
,
C.
,
Zhang
,
H.
,
Jiang
,
B.
, and
Li
,
K.
,
2019
, “
Aspect-Based Sentiment Analysis With Alternating Coattention Networks
,”
Inform. Process. Manage.
,
56
(
3
), pp.
463
478
. 10.1016/j.ipm.2018.12.004
43.
Han
,
Y.
, and
Moghaddam
,
M.
,
2019
, “
Analysis of Sentiment Expressions for Customer-Centric Design
,”
Expert Syst. Appl.
,
1
(
617
), pp.
43
73
.
44.
Desai
,
S.
,
Sinno
,
B.
,
Rosenfeld
,
A.
, and
Li
,
J. J.
,
2019
, “
Adaptive Ensembling: Unsupervised Domain Adaptation for Political Document Analysis
,”
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
,
Stroudsburg, PA
,
Nov. 3–7
, pp.
4717
4729
.
45.
Sarma
,
P. K.
,
Liang
,
Y.
, and
Sethares
,
W.
,
2019
, “
Shallow Domain Adaptive Embeddings for Sentiment Analysis
,”
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
,
Stroudsburg, PA
,
Nov. 3–7
, pp.
5548
5557
.
46.
Berard
,
A.
,
Calapodescu
,
I.
,
Dymetman
,
M.
,
Roux
,
C.
,
Meunier
,
J.-L.
, and
Nikoulina
,
V.
,
2019
, “
Machine Translation of Restaurant Reviews: New Corpus for Domain Adaptation and Robustness
,”
Proceedings of the 3rd Workshop on Neural Generation and Translation
,
Florence, Italy
,
July 28–Aug. 2
, pp.
168
176
.
47.
Ma
,
X.
,
Zeng
,
J.
,
Peng
,
L.
,
Fortino
,
G.
, and
Zhang
,
Y.
,
2019
, “
Modeling Multi-Aspects Within One Opinionated Sentence Simultaneously for Aspect-Level Sentiment Analysis
,”
Future Gen. Computer Syst.
,
93
(
3
), pp.
304
311
. 10.1016/j.future.2018.10.041
48.
Zhu
,
Y.
,
Kiros
,
R.
,
Zemel
,
R.
,
Salakhutdinov
,
R.
,
Urtasun
,
R.
,
Torralba
,
A.
, and
Fidler
,
S.
,
2015
, “
2015 IEEE International Conference on Computer Vision (ICCV)
,”
ICCV
,
Washington, DC
,
Dec
.
49.
Dai
,
A. M.
, and
Le
,
Q. V.
,
2015
, “
Advances in Neural Information Processing Systems 28
,”
NIPS 2015
,
USA
,
Aug
.
50.
Howard
,
J.
, and
Ruder
,
S.
,
2018
, “
Universal Language Model Fine-Tuning for Text Classification
,”
ACL 2018–56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)
,
Melbourne, Australia
,
July 15–20
, Vol.
1
, pp.
328
339
.
51.
Miller
,
G. A.
,
Beckwith
,
R.
,
Fellbaum
,
C.
,
Gross
,
D.
, and
Miller
,
K. J.
,
1990
, “
Introduction to WordNet: An Online Lexical Database*
,”
Int. J. Lexicography
,
3
(
4
), pp.
235
244
. 10.1093/ijl/3.4.235
52.
Papineni
,
K.
,
Roukos
,
S.
,
Ward
,
T.
, and
Zhu
,
W.-J.
,
2002
, “
Bleu: A Method for Automatic Evaluation of Machine Translation
,”
40th Annual Meeting of the Association for Computational Linguistics (ACL)
,
Philadelphia
,
July
, pp.
311
318
.
53.
Mirtalaie
,
M. A.
,
Hussain
,
O. K.
,
Chang
,
E.
, and
Hussain
,
F. K.
,
2017
, “
A Decision Support Framework for Identifying Novel Ideas in New Product Development From Cross-Domain Analysis
,”
Inform. Syst.
,
69
(
6
), pp.
59
80
. 10.1016/j.is.2017.04.003
54.
Nasukawa
,
T.
, and
Yi
,
J.
,
2003
, “
Sentiment Analysis: Capturing Favorability Using Natural Language Processing
,”
Proceedings of the 2nd International Conference on Knowledge Capture (K-CAP 2003)
,
Sanibel Island, FL
,
Oct. 23–25
, pp.
70
77
.
55.
Liu
,
B.
,
2009
,
Sentiment Analysis and Subjectivity
,
Cambridge University Press
.
56.
Fernández-Gavilanes
,
M.
,
Álvarez-López
,
T.
,
Juncal-Martínez
,
J.
,
Costa-Montenegro
,
E.
, and
González-Castaño
,
F. J.
,
2016
, “
Unsupervised Method for Sentiment Analysis in Online Texts
,”
Expert Syst. Appl.
,
58
(
1
), pp.
57
75
. 10.1016/j.eswa.2016.03.031
57.
Suryadi
,
D.
, and
Kim
,
H.
,
2018
, “
A Systematic Methodology Based on Word Embedding for Identifying the Relation Between Online Customer Reviews and Sales Rank
,”
ASME J. Mech. Design
,
140
(
12
), p.
121403
. 10.1115/1.4040913
58.
Archak
,
N.
,
Ghose
,
A.
, and
Ipeirotis
,
P. G.
,
2007
, “
13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
,”
13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
,
San Jose, CA
,
Aug. 12–15
.
59.
Kobayashi
,
N.
,
Inui
,
K.
, and
Matsumoto
,
Y.
,
2007
, “
Opinion Mining From Web Documents: Extraction and Structurization
,”
Trans. Japanese Soc. Artificial Intell.
,
22
(
2
), pp.
227
237
. 10.1527/tjsai.22.227
60.
Netzer
,
O.
,
Feldman
,
R.
,
Goldenberg
,
J.
, and
Fresko
,
M.
,
2012
, “
Mine Your Own Business: Market-Structure Surveillance Through Text Mining
,”
Marketi. Sci.
,
31
(
3
), pp.
521
543
. 10.1287/mksc.1120.0713
61.
Hu
,
M.
, and
Liu
,
B.
,
2004
,
Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data mining
,
Seattle, WA
,
August
.
62.
Wei
,
C. P.
,
Chen
,
Y. M.
,
Yang
,
C. S.
, and
Yang
,
C. C.
,
2010
, “
Understanding What Concerns Consumers: A Semantic Approach to Product Feature Extraction From Consumer Reviews
,”
Inform. Syst. e-Business Manage.
,
8
(
2
), pp.
149
167
. 10.1007/s10257-009-0113-9
63.
Abulaish
,
M.
,
Jahiruddin
,
Doja
,
M. N.
, and
Ahmad
,
T.
,
2009
,
Feature and Opinion Mining for Customer Review Summarization
, Vol.
5909
,
LNCS
,
USA
, pp.
219
224
.
64.
Mei
,
Q.
,
Ling
,
X.
,
Wondra
,
M.
,
Su
,
H.
, and
Zhai
,
C.
,
2007
, “
Topic Sentiment Mixture: Modeling Facets and Opinions in Weblogs
,”
Proceedings of the 16th International World Wide Web Conference (WWW '07)
,
USA
,
December
, pp.
171
180
.
65.
Ma
,
B.
,
Zhang
,
D.
,
Yan
,
Z.
, and
Kim
,
T.
,
2013
, “
An LDA and Synonym Lexicon Based Approach to Product Feature Extraction From Online Consumer Product Reviews
,”
J. Electron. Commerce Res.
,
14
(
4
), pp.
304
314
.
66.
Somprasertsri
,
G.
, and
Lalitrojwong
,
P.
,
2008
, “
Automatic Product Feature Extraction From Online Product Reviews Using Maximum Entropy With Lexical and Syntactic Features
,”
IEEE International Conference on Information Reuse and Integration, IEEE IRI-2008
,
Florida
,
July 13–15
.
67.
Somprasertsri
,
G.
, and
Lalitrojwong
,
P.
,
2010
, “
Mining Feature-Opinion in Online Customer Reviews for Opinion Summarization
,”
J. Univ. Comput. Sci.
,
16
(
6
), pp.
938
955
.
68.
Grishman
,
R.
, and
Sundheim
,
B.
,
1996
, “
Message Understanding Conference-6
,”
Proceedings of the 16th Conference on Computational Linguistics
,
Morristown, NJ
,
May 20–22
, p.
471
.
69.
Nadeau
,
D.
, and
Sekine
,
S.
,
2007
, “
A Survey of Named Entity Recognition and Classification
,”
Lingvisticæ Investigat.
,
30
(
1
), pp.
3
26
. 10.1075/li.30.1.03nad
70.
Bikel
,
D. M.
,
Miller
,
S.
,
Schwartz
,
R.
, and
Weischedel
,
R.
,
1997
, “
Nymble: A High-Performance Learning Name-Finder
,”
Proceedings of the Fifth Conference on Applied Natural Language Processing
,
Morristown, NJ
,
Aug. 1–2
, pp.
194
201
.
71.
Sekine
,
S.
, and
Sekine
,
S.
,
1998
, “
NYU: Description of the Japanese NE System Used for MET-2
,”
Proceedings of the Seventh Message Understanding Conference (MUC-7)
,
Fairfax, VA
,
Apr. 29–May 1
.
72.
Borthwick
,
A.
,
Sterling
,
J.
,
Agichtein
,
E.
, and
Grishman
,
R.
,
1998
, “
NYU: Description of the MENE Named Entity System as Used in MUC-7
,”
Seventh Message Understanding Conference (MUC-7): Proceedings of a Conference
,
Fairfax, VA
,
Apr. 29
.
73.
Asahara
,
M.
, and
Matsumoto
,
Y.
,
2003
, “
Japanese Named Entity Extraction With Redundant Morphological Analysis
,”
Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology—NAACL ’03
,
Morristown, NJ
,
Apr. 17
, pp.
8
15
.
74.
McCallum
,
A.
, and
Li
,
W.
,
2003
, “
Early Results for Named Entity Recognition With Conditional Random Fields, Feature Induction and Web-Enhanced Lexicons
,”
Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003
,
Morristown, NJ
,
Apr. 17
, pp.
188
191
.
75.
Nadeau
,
D.
,
Turney
,
P. D.
, and
Matwin
,
S.
,
2006
, “Unsupervised Named-Entity Recognition: Generating Gazetteers and Resolving Ambiguity,”
Lecture Notes in Computer Science (Including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
, Vol.
4013
,
LNAI, Springer Verlag
,
New York
, pp.
266
277
.
76.
Alfonseca
,
E.
,
Alfonseca
,
E.
, and
Manandhar
,
S.
,
2002
, “
An Unsupervised Method for General Named Entity Recognition And Automated Concept Discovery
,”
Proceedings of the 1st International Conference on General Wordnet
,
Aug. 2–14
.
77.
Shinyama
,
Y.
, and
Sekine
,
S.
,
2004
, “
Named Entity Discovery Using Comparable News Articles
,”
Proceedings of the 20th international conference on Computational Linguistics—COLING ’04
,
Morristown, NJ
,
Oct.
, p.
848es
.
78.
Bengio
,
Y.
,
Ducharme
,
R.
,
Vincent
,
P.
, and
Jauvin
,
C.
,
2003
, “
A Neural Probabilistic Language Model
,”
J. Mach. Learn. Res.
,
3
(
7
), pp.
1137
1155
.
79.
Mikolov
,
T.
,
Chen
,
K.
,
Corrado
,
G.
, and
Dean
,
J.
,
2006
, “
Distributed Representations of Words and Phrases and Their Compositionality
,”
1
(
1
), pp.
1
9
. arXiv preprint.http://arXiv.org/abs/1310.4546
80.
Le
,
Q.
, and
Mikolov
,
T.
,
2014
, “
Distributed Representations of Sentences and Documents
,”
International Conference on Machine Learning
,
Beijing, China
,
June 21–26
, pp.
1188
1196
.
81.
Pennington
,
J.
,
Socher
,
R.
, and
Manning
,
C. D.
,
2014
, “
Glove: Global Vectors for Word Representation
,”
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)
,
Doha, Qatar
,
Oct. 26–28
, pp.
1532
1543
.
82.
Young
,
T.
,
Hazarika
,
D.
,
Poria
,
S.
, and
Cambria
,
E.
,
2018
, “
Recent Trends in Deep Learning Based Natural Language Processing
,”
IEEE Computat. Intell. Magaz.
,
13
(
3
), pp.
55
75
. 10.1109/MCI.2018.2840738
83.
Quan
,
C.
, and
Ren
,
F.
,
2014
, “
Unsupervised Product Feature Extraction for Feature-Oriented Opinion Determination
,”
Inform. Sci.
,
272
(
7
), pp.
16
28
. 10.1016/j.ins.2014.02.063
84.
Kingma
,
D. P.
, and
Ba
,
J. L.
,
2014
, “
3rd International Conference for Learning Representations
,”
International Conference for Learning Representations
,
San Diego, CA
,
May 7–9
.
85.
Ulwick
,
A. W.
,
2002
, “
Turn Customer Input Into Innovation
,”
Harvard Business Rev.
,
80
(
5
), pp.
91
97
.
86.
Zhou
,
F.
,
Jiao
,
R. J.
, and
Linsey
,
J. S.
,
2015
, “
Latent Customer Needs Elicitation by Use Case Analogical Reasoning From Sentiment Analysis of Online Product Reviews
,”
ASME J. Mech. Des.
,
137
(
7
), p.
071401
. 10.1115/1.4030159
87.
He
,
L.
,
Chen
,
W.
,
Hoyle
,
C.
, and
Yannou
,
B.
,
2012
, “
Choice Modeling for Usage Context-Based Design
,”
ASME J. Mech. Des.
,
134
(
3
), p.
031007
. 10.1115/1.4005860
88.
Lin
,
J.
, and
Seepersad
,
C. C.
,
2007
, “
ASME 2006 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference
,”
IDETC/CIE
,
Las Vegas, NV
,
Sept. 4–7
, pp.
289
296
.
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