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Frontiers of Engineering Management

ISSN 2095-7513

ISSN 2096-0255(Online)

CN 10-1205/N

Postal Subscription Code 80-905

Front. Eng    2018, Vol. 5 Issue (4) : 479-486    https://doi.org/10.15302/J-FEM-2018035
RESEARCH ARTICLE
High-end equipment customer requirement analysis based on opinion extraction
Yuejin TAN, Yuren WANG(), Xin LU, Mengsi CAI, Bingfeng GE
College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
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Abstract

Acquisition and analysis of customer requirements are the essential steps in high-end equipment design. Considering that Internet and big data technologies are integrated into the manufacturing industry, we propose a method of analyzing customer requirements based on open-source data. First, online data are collected with focused crawlers and preprocessed to filter noise and duplicate. Then, user opinions are extracted based on the defined template, and users’ sentiments are analyzed. Based on the relationship between user sentiments and attribute parameters, the parameter range that satisfies customers can be obtained. The proposed method is evaluated by using an example of new energy vehicle to verify its availability and feasibility.

Keywords requirement analysis      opinion extraction      high-end equipment      new energy vehicle     
Corresponding Author(s): Yuren WANG   
Just Accepted Date: 14 August 2018   Online First Date: 25 September 2018    Issue Date: 29 November 2018
 Cite this article:   
Yuejin TAN,Yuren WANG,Xin LU, et al. High-end equipment customer requirement analysis based on opinion extraction[J]. Front. Eng, 2018, 5(4): 479-486.
 URL:  
https://academic.hep.com.cn/fem/EN/10.15302/J-FEM-2018035
https://academic.hep.com.cn/fem/EN/Y2018/V5/I4/479
Fig.1  Dependency parsing
Fig.2  High-end equipment customer requirement analysis process
1 Blecker T, Abdelkafi N, Kreutler G, Friedrich G (2004). An advisory system for customers’ objective needs elicitation in mass customization. In: Processing of the 4th Workshop on Information System for Mass Customization. 1–10
2 Chang Y M, Chen C W (2016). Kansei assessment of the constituent elements and the overall interrelations in car steering wheel design. International Journal of Industrial Ergonomics, 56: 97–105
https://doi.org/10.1016/j.ergon.2016.09.010
3 Chen Z, Wang L (2010). Personalized product configuration rules with dual formulations: A method to proactively leverage mass confusion. Expert Systems with Applications, 37(1): 383–392
https://doi.org/10.1016/j.eswa.2009.05.050
4 Choi Y, Cardie C, Riloff E, Patwardhan S (2005). Identifying sources of opinions with conditional random fields and extraction patterns. In: Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing. Association for Computational Linguistics.355–362
5 Hansen T, Scheer C, Loos P (2003). Product configurators in electronic commerce–extension of the configurator concept towards customer recommendation. In: Proceedings of the 2nd Interdisciplinary World Congress on Mass Customization and Personalization (MCP)
6 Hu M, Liu B (2004). Mining and summarizing customer reviews. In: Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. DBLP: 168–177
7 Huang M S, Tsai H C, Huang T H (2011). Applying Kansei engineering to industrial machinery trade show booth design. International Journal of Industrial Ergonomics, 41(1): 72–78
https://doi.org/10.1016/j.ergon.2010.10.002
8 Huang Y, Chen C H, Khoo L P (2012). Kansei clustering for emotional design using a combined design structure matrix. International Journal of Industrial Ergonomics, 42(5): 416–427
https://doi.org/10.1016/j.ergon.2012.05.003
9 Irsoy O, Cardie C (2014). Opinion mining with deep recurrent neural networks. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. 720–728
10 Jing Y G, Dan B, Zhang X M, Guo G (2010). Intelligent understanding approach of unstructured customer needs based on ontology. Computer Integrated Manufacturing Systems, 16(5): 1026–1033 (in Chinese)
11 Katiyar A, Cardie C (2016). Investigating lstms for joint extraction of opinion entities and relations. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. 1: 919–929
12 Khalid H M, Opperud A, Radha J K, Xu Q, Helander M G (2012). Elicitation and analysis of affective needs in vehicle design. Theoretical Issues in Ergonomics Science, 13(3): 318–334
https://doi.org/10.1080/1463922X.2010.506559
13 Kumar V R, Raghuveer K (2013). Dependency driven semantic approach to product features extraction and summarization using customer reviews. Advances in Intelligent Systems & Computing, 178: 225–238
https://doi.org/10.1007/978-3-642-31600-5_23
14 Lafferty J, McCallum A, Pereira F C (2001). Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of the 18 International Conference on Machine Learning, ICML. 1: 282–289
15 Liu P, Joty S, Meng H (2015). Fine-grained opinion mining with recurrent neural networks and word embeddings. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 1433–1443
16 Mavridou E, Kehagias D D, Tzovaras D, Hassapis G (2013). Mining affective needs of automotive industry customers for building a mass-customization recommender system. Journal of Intelligent Manufacturing, 24(2): 251–265
https://doi.org/10.1007/s10845-011-0579-4
17 Nagamachi M (2002). Kansei engineering as a powerful consumer-oriented technology for product development. Applied Ergonomics, 33(3): 289–294
https://doi.org/10.1016/S0003-6870(02)00019-4 pmid: 12164511
18 Piller F, Schubert P, Koch M, Möslein K (2005). Overcoming mass confusion: Collaborative customer co-design in online communities. Journal of Computer-Mediated Communication, 10(4): 00
https://doi.org/10.1111/j.1083-6101.2005.tb00271.x
19 Riloff E (1996). An empirical study of automated dictionary construction for information extraction in three domains. Artificial Intelligence, 85(1–2): 101–134
https://doi.org/10.1016/0004-3702(95)00123-9
20 Roy R, Goatman M, Khangura K (2009). User-centric design and Kansei Engineering. CIRP Journal of Manufacturing Science and Technology, 1(3): 172–178
https://doi.org/10.1016/j.cirpj.2008.10.007
21 Shi F, Sun S, Xu J (2012). Employing rough sets and association rule mining in KANSEI knowledge extraction. Information Sciences, 196: 118–128
https://doi.org/10.1016/j.ins.2012.02.006
22 Slywotzky A J (2000). The age of the choiceboard. Harvard Business Review, 78(1): 40–41
23 Walsh J, Godfrey S (2000). The Internet: A new era in customer service. European Management Journal, 18(1): 85–92
https://doi.org/10.1016/S0263-2373(99)00071-7
24 Yanagisawa H, Nakano S, Murakami T (2017). Advances in Affective and Pleasurable Design. New York: Springer International Publishing
25 Yang B, Cardie C (2012). Extracting opinion expressions with semi-markov conditional random fields. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Association for Computational Linguistics. 1335–1345
26 Yang B, Cardie C (2013). Joint inference for fine-grained opinion extraction. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics. 1: 1640–1649
27 Yi J, Nasukawa T, Bunescu R, Niblack W (2003). Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques. In: Proceedings of the 3rd IEEE International Conference on Data Mining. 427–434
28 Zhang F, Yang M, Liu W (2014). Using integrated quality function deployment and theory of innovation problem solving approach for ergonomic product design. Computers & Industrial Engineering, 76: 60–74
https://doi.org/10.1016/j.cie.2014.07.019
29 Zhou Q, Xia R, Zhang C (2016). Online shopping behavior study based on multi-granularity opinion mining: China versus America. Cognitive Computation, 8(4): 587–602
https://doi.org/10.1007/s12559-016-9384-x
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