隨著網際網路的蓬勃發展，越來越多消費者願意在網路上分享他們對新產品的看法及評價，而產品設計者為了瞭解客戶需求，往往需要瀏覽這類網路評論來尋找有用的資訊，並盡快設計出新產品來滿足市場需求。辨識客戶需求是產品開發階段一個很重要的步驟，但是數以百計的網路評論往往會讓設計者花費太多時間在辨識客戶需求上。因此，為了快速地辨識客戶需求並加速產品開發速度，本研究提出一個結合文字探勘及感性工學之數據導向的方法來協助產品設計。利用文字探勘從客戶評論中抓取關鍵字，並用感性工學的概念將客戶需求融入產品開發，最後根據感性工學的結果產生一個CAD的原型設計。本研究以腳踏車為例來驗證本研究提出之方法。在目前數據導向的趨勢下，本研究是第一個在產品開發過程中結合文字探勘及感性工學的研究。

With the rapid expansion of Web2.0, more and more people express their opinions and comments about products online. To understand and satisfy customer requirements, designers need to find out helpful information from online reviews and design a new one as fast as possible. It’s an important phase to identify customer requirements in product development process. However, popular products can have more than thousands of reviews, designers often spend a lot of time on identifying customer needs. Therefore, with an aim to meet customer requirements and speed up product development process, this research proposes a data-driven design method which combines text mining and Kansei engineering. Text mining is dedicated to extract the key words from customer reviews. Kansei engineering aims to translate customer needs into the product development domain. According to the result of Kansei engineering, a CAD model will be generated by proposed design automation system to visualize prototypes. Moreover, a case study of bike is provided to demonstrate the practical viability of proposed method. Under the trend of data-driven design, this is the first few studies that integrates text mining and Kansei engineering in product development process. It also reduces time and cost of product design through automation of repetitive design tasks. The design automation system is valuable for designers to identify customer needs and generate engineering drawing immediately.

1 Introduction 1
2 Literature Review 4
2.1 Text Mining 4
2.2 Data-driven Design (D3) 7
2.3 Kansei Engineering (KE) 10
2.4 Design Automation 13
3 Methodology 16
3.1 Text Pre-processing 17
3.2 Text Analysis 20
3.3 Kansei Engineering 22
3.3.1 Collection of Kansei Words 22
3.3.2 Applying Semantic Differential (SD) Scale 22
3.3.3 Quantification Theory Type 1 (QT1) 24
3.4 Design Automation 26
4 Case Study 28
4.1 Text Pre-processing 28
4.2 Text Analysis 29
4.3 Kansei Engineering 30
4.4 Design Automation 34
4.5 Validation 37
4.6 Discussion 39
5 Conclusion 43
6 Reference 45
7 Appendix 51

[1] Agard, B., & Kusiak, A. (2004). Data mining for subassembly selection. Journal of manufacturing science and engineering, 126(3), 627-631.
[2] Bing, L., Wong, T. L., & Lam, W. (2016). Unsupervised Extraction of Popular Product Attributes from E-Commerce Web Sites by Considering Customer Reviews. ACM Transactions on Internet Technology (TOIT), 16(2), 12.
[3] Barbier, G., & Liu, H. (2011). Data mining in social media. In Social network data analytics (pp. 327-352). Springer US.
[4] Chiu, M. C., & Lin, Y. H. (2016). Simulation based method considering design for additive manufacturing and supply chain: An empirical study of lamp industry. Industrial Management & Data Systems, 116(2), 322-348.
[5] Chung, J. C., Hwang, T. S., Wu, C. T., Jiang, Y., Wang, J. Y., Bai, Y., & Zou, H. (2000). Framework for integrated mechanical design automation. Computer-Aided Design, 32(5), 355-365.
[6] Carenini, G., Cheung, J. C. K., & Pauls, A. (2013). MULTI‐DOCUMENT SUMMARIZATION OF EVALUATIVE TEXT. Computational Intelligence, 29(4), 545-576.
[7] Christensen, K., Nørskov, S., Frederiksen, L., & Scholderer, J. (2016). In search of new product ideas: Identifying ideas in online communities by machine learning and text mining. Creativity and Innovation Management.
[8] Da Xu, L., Wang, C., Bi, Z., & Yu, J. (2012). AutoAssem: an automated assembly planning system for complex products. IEEE Transactions on Industrial Informatics, 8(3), 669-678.
[9] Ferreira, R., de Souza Cabral, L., Freitas, F., Lins, R. D., de França Silva, G., Simske, S. J., & Favaro, L. (2014). A multi-document summarization system based on statistics and linguistic treatment. Expert Systems with Applications, 41(13), 5780-5787.
[10] Fayyad, U.M., Piatetsky-Shapiro, G., and Smyth P. (1996). Knowledge discovery and data mining: Towards a unifying framework. Knowledge Discovery and Data Mining :82-88.
[11] Feldman, R. and Dagan, I. (1995). Knowledge discovery in textual databases. Proceeding of the First International Conference on Knowledge Discovery:112-117.
[12] Gegick, M., Rotella, P., and Xie, T. (2010). Identifying security bug reports via text mining: An industrial case study. Mining software repositories (MSR), 2010 7th IEEE working conference on. IEEE.
[13] Hartono, M., & Chuan, T. K. (2011). How the Kano model contributes to Kansei engineering in services. Ergonomics, 54(11), 987-1004.
[14] He, W., Zha, S., and Li, L. (2013). Social media competitive analysis and text mining: A case study in the pizza industry. International Journal of Information Management, 33(3):464-472.
[15] Hu, M., & Liu, B. (2004, August). Mining and summarizing customer reviews. Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, 168-177
[16] Hou, J.-L., Chuo, H.-C., and Sun, M.-T. (2004). Heuristic and integrated approach for technical document authority and authentication sequence determination. International Journal of Production Research, 42(9):1747-1768.
[17] Huang, Y., Chen, C. H., Wang, I. H. C., & Khoo, L. P. (2014). A product configuration analysis method for emotional design using a personal construct theory. International Journal of Industrial Ergonomics, 44(1), 120-130.
[18] Hsiao, Y. H., Chen, M. C., & Liao, W. C. (2017). Logistics service design for cross-border E-commerce using Kansei engineering with text-mining-based online content analysis. Telematics and Informatics, 34(4), 284-302.
[19] Jin, J., Liu, Y., Ji, P., & Liu, H. (2016). Understanding big consumer opinion data for market-driven product design. International Journal of Production Research, 54(10), 3019-3041.
[20] Jin, J., Ji, P., & Liu, Y. (2015). Translating online customer opinions into engineering characteristics in QFD: A probabilistic language analysis approach. Engineering Applications of Artificial Intelligence, 41, 115-127.
[21] Jindo, T., & Hirasago, K. (1997). Application studies to car interior of Kansei engineering. International journal of industrial ergonomics, 19(2), 105-114.
[22] Komozava, T., & Hayashi, C. (1976). A Statistical Method for Quantifying of Categorical Data and its Application to Medical Science.
[23] Kus̆ar, J., Duhovnik, J., Grum, J., & Starbek, M. (2004). How to reduce new product development time. Robotics and Computer-Integrated Manufacturing, 20(1), 1-15.
[24] Kim, H., Liu, Y., Wang, Y., & Wang, C. (2016). Special Issue: Data-Driven Design (D3). Journal of Mechanical Design, 138(12), 128002.
[25] Li, H., Ye, Q., & Law, R. (2013). Determinants of customer satisfaction in the hotel industry: an application of online review analysis. Asia Pacific Journal of Tourism Research, 18(7), 784-802.
[26] Liu, B. (2007). Web data mining: exploring hyperlinks, contents, and usage data. Springer Science & Business Media.
[27] Li, N. and Wu, D.D. (2010). Using text mining and sentiment analysis for online forum hotspot detection and forecast. Decision Support Systems, 48(2):354-368.
[28] Lewis, C.D. (1982). Industrial and business forecasting methods. London: Butterworths.
[29] Nakada, K. (1997). Kansei engineering research on the design of construction machinery. International Journal of Industrial Ergonomics, 19(2), 129-146.
[30] Nagamachi, M. (2002). Kansei engineering as a powerful consumer-oriented technology for product development. Applied ergonomics, 33(3), 289-294.
[31] Nagamachi, M. (2008). Perspectives and the new trend of Kansei/affective engineering. The TQM Journal, 20(4), 290-298.
[32] Nanda, P., Bos, J., Kramer, K. L., Hay, C., & Ignacz, J. (2008). Effect of smartphone aesthetic design on users' emotional reaction: An empirical study. The TQM Journal, 20(4), 348-355.
[33] Nagamachi, M. (1995). Kansei engineering: a new ergonomic consumer-oriented technology for product development. International Journal of industrial ergonomics, 15(1), 3-11.
[34] Nishino, T. (2001). Exercises on Kansei Engineering. Hiroshima International University.
[35] Mashhadi, A. R., Esmaeilian, B., Cade, W., Wiens, K., & Behdad, S. (2016). Mining consumer experiences of repairing electronics: Product design insights and business lessons learned. Journal of Cleaner Production, 137, 716-727.
[36] Ragatz, G. L., Handfield, R. B., & Scannell, T. V. (1997). Success factors for integrating suppliers into new product development. Journal of product innovation management, 14(3), 190-202.
[37] Rahaju, D. E. S., & Dewi, D. R. S. (2011). Dealing with dissatisfaction measure in QFD model to derive target of engineering characteristics. International Journal of Industrial Engineering, 18(12).
[38] Red, E., French, D., Hepworth, A., Jensen, G., & Stone, B. (2014). Multi-User Computer-Aided Design and Engineering Software Applications. In Cloud-Based Design and Manufacturing (CBDM) (pp. 25-62). Springer International Publishing.
[39] Reddy, E. J., Sridhar, C. N. V., & Rangadu, V. P. (2016). Research and Development of Knowledge Based Intelligent Design System for Bearings Library Construction Using SolidWorks API. In Intelligent Systems Technologies and Applications (pp. 311-319). Springer International Publishing.
[40] Sainter, P., Oldham, K., & Larkin, A. (2000, June). Achieving benefits from knowledge-based engineering systems in the longer term as well as in the short term. In Proceedings of: 6th International Conference on Concurrent Enterprising.
[41] Seo, W., Yoon, J., Park, H., Coh, B. Y., Lee, J. M., & Kwon, O. J. (2016). Product opportunity identification based on internal capabilities using text mining and association rule mining. Technological Forecasting and Social Change, 105, 94-104.
[42] Schütte, S., & Eklund, J. (2005). Design of rocker switches for work-vehicles—an application of Kansei Engineering. Applied ergonomics, 36(5), 557-567.
[43] Somprasertsri, G., & Lalitrojwong, P. (2010). Mining Feature-Opinion in Online Customer Reviews for Opinion Summarization. J. UCS, 16(6), 938-955.
[44] Sato, N., Anse, M., & Tabe, T. (2007). A Method for Constructing a Movie-Selection Support System Based on Kansei Engineering. In Symposium on Human Interface and the Management of Information (pp. 526-534). Springer Berlin Heidelberg.
[45] Schütte, S., & Eklund, J. (2005). Design of rocker switches for work-vehicles—an application of Kansei Engineering. Applied ergonomics, 36(5), 557-567.
[46] Shieh, M. D., & Yeh, Y. E. (2013). Developing a design support system for the exterior form of running shoes using partial least squares and neural networks. Computers & Industrial Engineering, 65(4), 704-718.
[47] Trappey, A. J., Trappey, C. V., Chang, A. C., & Chen, L. W. (2016). Using Web Mining and Perceptual Mapping to Support Customer-Oriented Product Positions and Designs. In TRANSDISCIPLINARY ENGINEERING: CROSSING BOUNDARIES (Vol. 4, pp. 533-542).
[48] Tseng, Y.-H., Lin, C.-J. and Lin, Y.-I (2007). Text mining techniques for patent analysis. Information Processing and Management, 43(5):1216-1247.
[49] Tanoue, C., Ishizaka, K., & Nagamachi, M. (1997). Kansei Engineering: A study on perception of vehicle interior image. International Journal of Industrial Ergonomics, 19(2), 115-128.
[50] Tucker, C. S., & Kim, H. M. (2009). Data-driven decision tree classification for product portfolio design optimization. Journal of Computing and Information Science in Engineering, 9(4), 041004.
[51] Tucker, C., & Kim, H. (2011). Predicting emerging product design trend by mining publicly available customer review data. In DS 68-6: Proceedings of the 18th International Conference on Engineering Design (ICED 11), Impacting Society through Engineering Design, Vol. 6: Design Information and Knowledge, Lyngby/Copenhagen, Denmark, 15.-19.08. 2011.
[52] Verhagen, W. J., Bermell-Garcia, P., van Dijk, R. E., & Curran, R. (2012). A critical review of Knowledge-Based Engineering: An identification of research challenges. Advanced Engineering Informatics, 26(1), 5-15.
[53] Wang, L., Youn, B. D., Azarm, S., & Kannan, P. K. (2011, January). Customer-driven product design selection using web based user-generated content. In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (pp. 405-419). American Society of Mechanical Engineers.
[54] Wang, C. C., Wang, Y., & Yuen, M. M. (2005). Design automation for customized apparel products. Computer-aided design, 37(7), 675-691.
[55] Wang, C. C., Hui, K. C., & Tong, K. M. (2007). Volume parameterization for design automation of customized free-form products. IEEE transactions on automation science and engineering, 4(1), 11-21.
[56] Wu, T. Y., Hsu, Y., & Lee, G. A. (2015). The effect of product appearances on consumer emotions and behaviors: a perspective of involvement. Journal of Industrial and Production Engineering, 32(8), 486-499.
[57] Wang, D., Zhu, S., & Li, T. (2013). SumView: A Web-based engine for summarizing product reviews and customer opinions. Expert Systems with Applications, 40(1), 27-33.
[58] Wang, K. C. (2011). A hybrid Kansei engineering design expert system based on grey system theory and support vector regression. Expert Systems with Applications, 38(7), 8738-8750.
[59] Wang, C. H. (2015). Integrating Kansei engineering with conjoint analysis to fulfil market segmentation and product customisation for digital cameras. International Journal of Production Research, 53(8), 2427-2438.
[60] Yang, C. C. (2011). Constructing a hybrid Kansei engineering system based on multiple affective responses: Application to product form design. Computers & Industrial Engineering, 60(4), 760-768.
[61] Yang, S. M., Nagamachi, M., & Lee, S. Y. (1999). Rule-based inference model for the Kansei Engineering System. International Journal of Industrial Ergonomics, 24(5), 459-471.
[62] Yoon, B., Park, I., & Coh, B. Y. (2014). Exploring technological opportunities by linking technology and products: Application of morphology analysis and text mining. Technological Forecasting and Social Change, 86, 287-303.
[63] Zhan, J., Loh, H. T., & Liu, Y. (2009). Gather customer concerns from online product reviews–A text summarization approach. Expert Systems with Applications, 36(2), 2107-2115.
[64] Zwicky, F. (1967). The morphological approach to discovery, invention, research and construction. In New methods of thought and procedure (pp. 273-297). Springer Berlin Heidelberg.
[65] Zhu, F., & Zhang, X. (2010). Impact of online consumer reviews on sales: The moderating role of product and consumer characteristics. Journal of marketing, 74(2), 133-148.