Identification of the to-be-improved product features based on online reviews for product redesign

References

• Abdul-Mageed, Muhammad, Mona Diab, and Sandra Kübler. 2014. “SAMAR: Subjectivity and Sentiment Analysis for Arabic Social Media.” Computer Speech & Language 28 (1): 20–37. doi: 10.1016/j.csl.2013.03.001
   Web of Science ®Google Scholar
• Balahur, Alexandra, Rada Mihalcea, and Andrés Montoyo. 2014. “Computational Approaches to Subjectivity and Sentiment Analysis: Present and Envisaged Methods and Applications.” Computer Speech & Language 28: 1–6. doi: 10.1016/j.csl.2013.09.003
   Web of Science ®Google Scholar
• Bickart, B., and R. M. Schindler. 2001. “Internet Forums as Influential Sources of Consumer Information.” Journal of Interactive Marketing 15 (3): 31–40. doi: 10.1002/dir.1014
   Google Scholar
• Booysen, G. J., L. J. Barnard, M. Truscott, and D. J. de Beer. 2006. “Anaesthetic Mouthpiece Development Through QFD and Customer Interaction with Functional Prototypes.” Rapid Prototyping Journal 12 (4): 189–197. doi: 10.1108/13552540610682697
   Web of Science ®Google Scholar
• Bovea, M., and B. Wang. 2007. “Redesign Methodology for Developing Environmentally Conscious Products.” International Journal of Production Research 45 (18-19): 4057–4072. doi: 10.1080/00207540701472678
   Web of Science ®Google Scholar
• Brenner, W., and F. Uebernickel. 2016. Design Thinking for Innovation: Research and Practice. Freiberg: Springer.
   Google Scholar
• Cataldi, M., A. Ballatore, I. Tiddi, and M. A. Aufaure. 2013. “Good Location, Terrible Food: Detecting Feature Sentiment in User-Generated Reviews.” Social Network Analysis & Mining 3 (4): 1149–1163. doi: 10.1007/s13278-013-0119-7
   Google Scholar
• Chen, L., A. Macwan, and S. Li. 2007. “Model-based Rapid Redesign Using Decomposition Patterns.” Journal of Mechanical Design 129 (3): 283–294. doi: 10.1115/1.2406099
   Web of Science ®Google Scholar
• Chen, L., L. Qi, and F. Wang. 2012. “Comparison of Feature-Level Learning Methods for Mining Online Consumer Reviews.” Expert Systems with Applications 39 (10): 9588–9601. doi: 10.1016/j.eswa.2012.02.158
   Web of Science ®Google Scholar
• Das, S. R., and M. Y. Chen. 2007. “Yahoo! for Amazon: Sentiment Extraction From Small Talk on the web.” Management Science 53 (9): 1375–1388. doi: 10.1287/mnsc.1070.0704
   Web of Science ®Google Scholar
• Dave, K., S. Lawrence, and D. M. Pennock. 2003. “Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews.” In Proceedings of the 12th International Conference on World Wide Web, 519–528. Budapest: ACM.
   Google Scholar
• Ding, X., B. Liu, and P. S. Yu. 2008. “A Holistic Lexicon-Based Approach to Opinion Mining.” In Proceedings of the 2008 International Conference on Web Search and Data Mining, 231–240. Palo Alto: ACM.
   Google Scholar
• Gangurde, Sanjay R., and M. M. Akarte. 2013. “Customer Preference Oriented Product Design Using AHP-Modified TOPSIS Approach.” Benchmarking: An International Journal 20 (4): 549–564. doi: 10.1108/BIJ-08-2011-0058
   Google Scholar
• Gautam, N., and N. Singh. 2008. “Lean Product Development: Maximizing the Customer Perceived Value Through Design Change (Redesign).” International Journal of Production Economics 114 (1): 313–332. doi: 10.1016/j.ijpe.2006.12.070
   Web of Science ®Google Scholar
• Geyer, Felix, J. Lehnen, and C. Herstatt. 2018. “Customer Need Identification Methods in New Product Development: What Works ‘Best’?” International Journal of Innovation & Technology Management 15 (01): 1850008. doi: 10.1142/S0219877018500086
   Google Scholar
• Hämäläinen, H. 2015. “Customer Need Assessment for Differentiation in Building Information Modeling.” Science 263 (5153): 1612–1615.
   Google Scholar
• Hu, M., and B. Liu. 2004a. “Mining and Summarizing Customer Reviews.” In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 168–177. Seattle, WA: ACM.
   Google Scholar
• Hu, M., and B. Liu. 2004b. “Mining Opinion Features in Customer Reviews.” In Proceedings of the 19th National Conference on Artificial Intelligence. Vol. 69, 755–760. San Jose, CA, USA: AAAI Press.
   Google Scholar
• Jacquemin, C., and D. Bourigault. 2003. “Term Extraction and Automatic Indexing.” In The Oxford Handbook of Computational Linguistics, edited by R. Mitkov, 599–615. Oxford, UK: Oxford University Press.
   Google Scholar
• Jiang, J. J., and D. W. Conrath. 1997. “Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy.” In Proceedings of 10th International Conference on Research in Computational Linguistics, 19–33. Taiwan: ROCLING.
   Google Scholar
• Jin, W., H. H. Ho, and R. K. Srihari. 2009. “OpinionMiner: A Novel Machine Learning System for Web Opinion Mining and Extraction.” In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1195–1204. Paris: ACM.
   Google Scholar
• Leavy, B. 2010. “Design Thinking – a new Mental Model of Value Innovation.” Strategy & Leadership 38 (3): 5–14. doi: 10.1108/10878571011042050
   Google Scholar
• Leonard, D., and J. F. Rayport. 1997. “Spark Innovation Through Empathic Design.” Harvard Business Review 75: 102–115.
   PubMed Web of Science ®Google Scholar
• Li, Z. S., F. H. Kou, X. C. Cheng, and T. Wang. 2006. “Model-based Product Redesign.” International Journal of Computer Science and Network Security 6 (1A): 100.
   Google Scholar
• Liedtka, J. 2015. “Perspective: Linking Design Thinking with Innovation Outcomes Through Cognitive Bias Reduction.” Journal of Product Innovation Management 32 (6): 925–938. doi: 10.1111/jpim.12163
   Web of Science ®Google Scholar
• Liu, C., A. Ramirez-Serrano, and G. Yin. 2012. “An Optimum Design Selection Approach for Product Customization Development.” Journal of Intelligent Manufacturing 23 (4): 1433–1443. doi: 10.1007/s10845-010-0473-5
   Web of Science ®Google Scholar
• Mallik, S., and D. Chhajed. 2006. “Optimal Temporal Product Introduction Strategies Under Valuation Changes and Learning.” European Journal of Operational Research 172 (2): 430–452. doi: 10.1016/j.ejor.2004.09.053
   Web of Science ®Google Scholar
• Miller, G. A. 1995. “WordNet: a Lexical Database for English.” Communications of the Acm 38 (11): 39–41. doi: 10.1145/219717.219748
   Web of Science ®Google Scholar
• Pang, Bo, and Lillian Lee. 2004. “A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts.” In Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, 271. Barcelona: Association for Computational Linguistics.
   Google Scholar
• Pang, B., L. Lee, and S. Vaithyanathan. 2002. “Thumbs up? Sentiment Classification Using Machine Learning Techniques.” In Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing. Vol. 10, 79–86. Philadelphia, PA: Association for Computational Linguistics.
   Google Scholar
• Pnueli, Y., and E. Zussman. 1997. “Evaluating the End-of-Life Value of a Product and Improving It by Redesign.” International Journal of Production Research 35 (4): 921–942. doi: 10.1080/002075497195452
   Web of Science ®Google Scholar
• Putthividhya, D. P., and J. Hu. 2011. “Bootstrapped Named Entity Recognition for Product Attribute Extraction.” In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, 1557–1567. Edinburgh: Association for Computational Linguistics.
   Google Scholar
• Rai, R. 2012. “Identifying Key Product Attributes and Their Importance Levels from Online Customer Reviews.” Proceedings of the 2012 ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC/CIE2012), Chicago, IL, USA.
   Google Scholar
• Resnik, P. 1995. “Using Information Content to Evaluate Semantic Similarity in a Taxonomy.” In Proceedings of the 14th International Joint Conference on Artificial Intelligence, 448–453. Montreal: Morgan Kaufmann Publishers Inc.
   Google Scholar
• Satapathy, Ranjan, Erik Cambria, and Amir Hussain. 2018. Sentiment Analysis in the Bio-Medical Domain: Techniques, Tools, and Applications. Vol. 7. New York: Springer.
   Google Scholar
• Seco, N., T. Veale, and J. Hayes. 2004. “An Intrinsic Information Content Metric for Semantic Similarity in WordNet.” In Proceedings of the 16th European Conference on Artificial Intelligence, 1089–1090. Valencia: IOS Press.
   Google Scholar
• Serrano-Guerrero, Jesus, Jose A. Olivas, Francisco P. Romero, Enrique Herrera-Viedma. 2015. “Sentiment Analysis: A Review and Comparative Analysis of Web Services.” Information Sciences 311: 18–38. doi: 10.1016/j.ins.2015.03.040
   Web of Science ®Google Scholar
• Smith, S., G. Smith, and Y. T. Shen. 2012. “Redesign for Product Innovation.” Design Studies 33 (2): 160–184. doi: 10.1016/j.destud.2011.08.003
   Web of Science ®Google Scholar
• Snyder, Benjamin, and Regina Barzilay. 2007. “Multiple Aspect Ranking Using the Good Grief Algorithm.” Proceedings of the Joint Human Language Technology/North American Chapter of the ACL Conference (HLT-NAACL): 300–307.
   Google Scholar
• Söderman, M. 2005. “Virtual Reality in Product Evaluations with Potential Customers: An Exploratory Study Comparing Virtual Reality with Conventional Product Representations.” Journal of Engineering Design 16 (3): 311–328. doi: 10.1080/09544820500128967
   Web of Science ®Google Scholar
• Stevenson, Ryan, Joseph Mikels, and Thomas James. 2007. “Characterization of the Affective Norms for English Words by Discrete Emotional Categories.” Behavior Research Methods 39 (4): 1020–1024. doi: 10.3758/BF03192999
   PubMed Web of Science ®Google Scholar
• Szekely, G. J., and M. L. Rizzo. 2005. “Hierarchical Clustering via Joint Between-Within Distances: Extending Ward’s Minimum Variance Method.” Journal of Classification 22 (2): 151–183. doi: 10.1007/s00357-005-0012-9
   Web of Science ®Google Scholar
• Turney, P. D. 2002. “Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews.” In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL 2002), 417–424. Philadelphia, PA: Association for Computational Linguistics.
   Google Scholar
• Zhou, F., Y. Ji, and R. J. Jiao. 2013. “Affective and Cognitive Design for Mass Personalization: Status and Prospect.” Journal of Intelligent Manufacturing 24 (5): 1047–1069. doi: 10.1007/s10845-012-0673-2
   Web of Science ®Google Scholar
• Zhou, F., J. R. Jiao, D. Schaefer, and S. Chen. 2010. “Hybrid Association Mining and Refinement for Affective Mapping in Emotional Design.” Journal of Computing & Information Science in Engineering, ASME Transactions 10 (3): 789–801.
   Web of Science ®Google Scholar