0
Review Article

Review on Recent Advances in Information Mining From Big Consumer Opinion Data for Product Design

[+] Author and Article Information
Jian Jin

School of Government,
Department of Information Management,
Beijing Normal University,
Beijing 100875, China

Ying Liu

Mem. ASME,
Mechanical and Manufacturing Engineering,
School of Engineering,
Cardiff University,
Cardiff CF24 3AA, UK
e-mail: LiuY81@cardiff.ac.uk

Ping Ji, C. K. Kwong

Department of Industrial and
Systems Engineering,
The Hong Kong Polytechnic University,
Hong Kong SAR,
China

1Corresponding author.

Contributed by the Computers and Information Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received January 21, 2018; final manuscript received August 2, 2018; published online September 17, 2018. Assoc. Editor: Matthew I. Campbell.

J. Comput. Inf. Sci. Eng 19(1), 010801 (Sep 17, 2018) (19 pages) Paper No: JCISE-18-1024; doi: 10.1115/1.4041087 History: Received January 21, 2018; Revised August 02, 2018

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.

FIGURES IN THIS ARTICLE
<>
Copyright © 2019 by ASME
Your Session has timed out. Please sign back in to continue.

References

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.
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.
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.
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.
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.
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.759–762.
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.
Miao, Q. , Li, Q. , and Dai, R. , 2009, “ AMAZING: A Sentiment Mining and Retrieval System,” Expert Syst. Appl., 36(3), pp. 7192–7198. [CrossRef]
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.
Zhang, R. , and Tran, T. , 2011, “ An Information Gain-Based Approach for Recommending Useful Product Reviews,” Knowl. Inf. Syst., 26(3), pp. 419–434. [CrossRef]
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.
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. [CrossRef]
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.
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. [CrossRef]
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.
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.
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. 219–230 https://dl.acm.org/citation.cfm?id=1341560.
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.
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.
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.
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.
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.
Morales, A. , Sun, H. , and Yan, X. , 2013, “ Synthetic Review Spamming and Defense,” WWW'13 Companion, Rio de Janeiro, Brazil, pp. 155–56.
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. [CrossRef]
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Yao, Y. , and Sun, A. , 2014, “ Product Name Recognition and Normalization in Internet Forums,” SIGIR'14, Gold Coast, Australia, July 6–11.
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.
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.
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
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. 231–240 https://dl.acm.org/citation.cfm?id=1341561.
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.
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.
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
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
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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. [CrossRef]
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.
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. [CrossRef]
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. [CrossRef]
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. 1020–1025 https://dl.acm.org/citation.cfm?id=2472547.
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.
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.
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.
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.
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.
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.
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. [CrossRef]
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.
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
Popescu, A.-M. , and Etzioni, O. , 2005, “ Extracting Product Features and Opinions From Reviews,” EMNLP'05, pp. 339–346.
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.
Stoyanov, V. , and Cardie, C. , 2008, “ Topic Identification for Fine-Grained Opinion Analysis,” COLING'08, Manchester, UK, Aug. 18–22, pp. 817–824.
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.
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. 375–384 https://dl.acm.org/citation.cfm?id=1646003&dl=ACM&coll=DL.
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.
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
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.
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.
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. [CrossRef]
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.
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.
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.
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
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.
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.
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.
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.
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.
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
Esuli, A. , and Sebastiani, F. , 2006, “ Determining Term Subjectivity and Term Orientation for Opinion Mining,” EACL'06, Stanford, CA, pp. 193–200 https://pdfs.semanticscholar.org/af5c/4034493461af13a7f5480e081becf0218511.pdf.
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. [CrossRef]
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.
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.
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.
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. [CrossRef]
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.
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.
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.
Wan, X. , 2011, “ Bilingual Co-Training for Sentiment Classification of Chinese Product Reviews,” Comput. Linguist., 37(3), pp. 587–616. [CrossRef]
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.
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.
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. [CrossRef]
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
Das, A. , and Bandyopadhyay, S. , 2010, “ Topic-Based Bengali Opinion Summarization,” COLING'10, Beijing, China, pp. 232–240.
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.
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.
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.
Lappas, T. , Crovella, M. , and Terzi, E. , 2012, “ Selecting a Characteristic Set of Reviews,” KDD'12, Beijing, China, pp. 832–840.
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.
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.
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.
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. 163–172 https://dl.acm.org/citation.cfm?id=2124316.
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.
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.
Mukherjee, A. , and Liu, B. , 2012, “ Mining Contentions From Discussions and Debates,” KDD'12, Beijing, China, pp. 841–849.
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. 241–248 https://dl.acm.org/citation.cfm?id=1599112.
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.
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. [CrossRef]
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
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
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.
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.
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.
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.
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.
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.
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.
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.
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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.
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. [CrossRef]
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.
Chan, L.-K. , and Wu, M.-L. , 2002, “ Quality Function Deployment: A Literature Review,” Eur. J. Oper. Res., 143(3), pp. 463–497. [CrossRef]
Aboulafia, A. , and Bannon, L. J. , 2004, “ Understanding Affect in Design: An Outline Conceptual Framework,” Theor. Issues Ergonom. Sci., 5 (1), pp. 4–15. [CrossRef]
Barnes, C. , and Lillford, S. P. , 2009, “ Decision Support for the Design of Affective Products,” J. Eng. Des., 20(5), pp. 477–492. [CrossRef]
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. [CrossRef]
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. [CrossRef]
Khalid, H. M. , 2006, “ Embracing Diversity in User Needs for Affective Design,” Appl. Ergonom., 37(4), pp. 409–418. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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.
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
Chan, K. Y. , and Engelke, U. , 2017, “ Varying Spread Fuzzy Regression for Affective Quality Estimation,” IEEE Trans. Fuzzy Syst., 25(3), pp. 594–613. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
Akay, D. , and Kurt, M. , 2008, “ A Neuro-Fuzzy Based Approach to Affective Design,” Int. J. Adv. Manuf. Technol., 40(5), p. 425.
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
He, B. , Song, W. , and Wang, Y. , “ Computational Conceptual Design Using Space Matrix,” ASME J. Comput. Inf. Sci. Eng., 15(1), p. 011004. [CrossRef]
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. [CrossRef]
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. [CrossRef]
Matthews, P. C. , 2008, “ A Bayesian Support Tool for Morphological Design,” Adv. Eng. Inf., 22(2), pp. 236–253. [CrossRef]
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. [CrossRef]
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. [CrossRef]
Kroll, E. , 2013, “ Design Theory and Conceptual Design: Contrasting Functional Decomposition and Morphology With Parameter Analysis,” Res. Eng. Des., 24(2), pp. 165–183. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
Griffin, A. , and Hauser, J. R. , 1993, “ The Voice of the Customer,” Marketing Sci., 12(1), pp. 1–27. [CrossRef]
Gustafsson, A. , and Gustafsson, N. , 1994, “ Exceeding Customer Expectations,” Sixth Symposium on Quality Function Deployment, pp. 52–57.
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
Saaty, T. L. , 1980, The Analytic Hierarchy Process, McGraw-Hill, New York.
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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.
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. [CrossRef]
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. [CrossRef]
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.
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. [CrossRef]
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.
Kang, J. , and Lee, H. , 2017, “ Modeling User Interest in Social Media Using News Media and Wikipedia,” Inf. Syst., 65, pp. 52–64. [CrossRef]
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.
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.
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.
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.
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.
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
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.
Iyengar, R. , and Van den Bulte, C. , 2011, “ Opinion Leadership and Social Contagion in New Product Diffusion,” Marketing Sci., 30(2), pp. 195–212. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
(Roger) Jiao, J. , Yiyang, Z. , and Helander, M. , 2006, “ A Kansei Mining System for Affective Design,” Expert Syst. Appl., 30(4), pp. 658–673. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
Akao, Y. , 1990, Quality Function Deployment: Integrating Customer Requirements Into Product Design, Productivity Press, Boston, MA.
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. [CrossRef]
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. [CrossRef]
Liu, Y.-C. , Chakrabarti, A. , and Bligh, T. , 2003, “ Towards an ‘Ideal’ Approach for Concept Generation,” Des. Stud., 24(4), pp. 341–355. [CrossRef]
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. [CrossRef]
Liu, A. , and Lu, S. C.-Y. , 2014, “ Alternation of Analysis and Synthesis for Concept Generation,” CIRP Ann., 63(1), pp. 177–180. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
Cao, D. , Li, Z. , and Ramani, K. , 2011, “ Ontology-Based Customer Preference Modeling for Concept Generation,” Adv. Eng. Inf., 25(2), pp. 162–176. [CrossRef]
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. [CrossRef]
Liu, A. , and Lu, S. C.-Y. , 2016, “ A Crowdsourcing Design Framework for Concept Generation,” CIRP Ann., 65(1), pp. 177–180. [CrossRef]
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.
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
Farnsworth, M. , and Tomiyama, T. , 2014, “ Capturing, Classification and Concept Generation for Automated Maintenance Tasks,” CIRP Ann., 63(1), pp. 149–152. [CrossRef]
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. [CrossRef]
Dong, A. , Lovallo, D. , and Mounarath, R. , 2015, “ The Effect of Abductive Reasoning on Concept Selection Decisions,” Des. Stud., 37, pp. 37–58. [CrossRef]
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. [CrossRef]
Noguchi, H. , 1997, “ An Idea Generation Support System for Industrial Designers (Idea Sketch Processor),” Knowl.-Based Syst., 10(1), pp. 37–42. [CrossRef]
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. [CrossRef]
Ko, Y.-T. , 2017, “ Modeling a Hybrid-Compact Design Matrix for New Product Innovation,” Comput. Ind. Eng., 107(Suppl. C), pp. 345–59. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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.
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.
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
Nonaka, I. , Krogh, G. , and Voelpel, S. , 2006, “ Organizational Knowledge Creation Theory: Evolutionary Paths and Future Advances,” Org. Stud., 27(8), pp. 1179–1208. [CrossRef]
Nonaka, I. , and Takeuchi, H. , 1995, The Knowledge-Creating Company, Oxford University Press, New York.
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
Vladimir, H. , Sokol, O. , and Cerny, M. , 2017, “ Clustering Retail Products Based on Customer Behaviour,” Appl. Soft Comput., 60, pp. 752–762. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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.
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.
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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.
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
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. [CrossRef]
Tucker, C. S. , and Kim, H. M. , 2011, “ Trend Mining for Predictive Product Design,” ASME J. Mech. Des., 133(11), p. 111008. [CrossRef]
Ma, J. , Kwak, M. , and Kim, H. M. , 2014, “ Demand Trend Mining for Predictive Life Cycle Design,” J. Cleaner Prod., 68, pp. 189–199. [CrossRef]
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. [CrossRef]
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.
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.
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/
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/
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/

Figures

Grahic Jump Location
Fig. 1

A framework of classical procedure regarding information mining from big consumer opinion data for product design

Tables

Errata

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In