Hostname: page-component-78c5997874-m6dg7 Total loading time: 0 Render date: 2024-11-14T15:17:38.126Z Has data issue: false hasContentIssue false
  • English
  • Français

Intelligent product redesign strategy with ontology-based fine-grained sentiment analysis

Published online by Cambridge University Press:  21 July 2021

Siyu Zhu Open the ORCID record for Siyu Zhu [Opens in a new window] ,
Jin Qi ,
Jie Hu  and
Haiqing Huang
Siyu Zhu
Affiliation:
School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai, China
Jin Qi*
Affiliation:
School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai, China
Jie Hu*
Affiliation:
School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai, China
Haiqing Huang
Affiliation:
School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai, China
*
Authors for correspondence: Jin Qi, E-mail: [email protected]; Jie Hu, E-mail: [email protected]
Authors for correspondence: Jin Qi, E-mail: [email protected]; Jie Hu, E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

With the increasing demand for a personalized product and rapid market response, many companies expect to explore online user-generated content (UGC) for intelligent customer hearing and product redesign strategy. UGC has the advantages of being more unbiased than traditional interviews, yielding in-time response, and widely accessible with a sheer volume. From online resources, customers’ preferences toward various aspects of the product can be exploited by promising sentiment analysis methods. However, due to the complexity of language, state-of-the-art sentiment analysis methods are still not accurate for practice use in product redesign. To tackle this problem, we propose an integrated customer hearing and product redesign system, which combines the robust use of sentiment analysis for customer hearing and coordinated redesign mechanisms. Ontology and expert knowledges are involved to promote the accuracy. Specifically, a fuzzy product ontology that contains domain knowledges is first learned in a semi-supervised way. Then, UGC is exploited with a novel ontology-based fine-grained sentiment analysis approach. Extracted customer preference statistics are transformed into multilevels, for the automatic establishment of opportunity landscapes and house of quality table. Besides, customer preference statistics are interactively visualized, through which representative customer feedbacks are concurrently generated. Through a case study of smartphone, the effectiveness of the proposed system is validated, and applicable redesign strategies for a case product are provided. With this system, information including customer preferences, user experiences, using habits and conditions can be exploited together for reliable product redesign strategy elicitation.

Keywords

Fuzzy product ontologyhouse of qualityproduct redesignrepresentative feedbacksentiment analysis
Type
Research Article
Information
AI EDAM , Volume 35 , Issue 3 , August 2021 , pp. 295 - 315
Check for updates
Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ali, F, Kwak, K-S and Kim, Y-G (2016) Opinion mining based on fuzzy domain ontology and support vector machine: a proposal to automate online review classification. Applied Soft Computing 47, 235–250.10.1016/j.asoc.2016.06.003CrossRefGoogle Scholar
Alrababah, SAA, Gan, KH and Tan, T-P (2017) Mining opinionated product features using WordNet lexicographer files. Journal of Information Science 43, 769–785.10.1177/0165551516667651CrossRefGoogle Scholar
Al-Smadi, M, Qawasmeh, O, Al-Ayyoub, M, Jararweh, Y and Gupta, B (2018) Deep recurrent neural network vs. support vector machine for aspect-based sentiment analysis of Arabic hotels’ reviews. Journal of Computational Science 27, 386–393.10.1016/j.jocs.2017.11.006CrossRefGoogle Scholar
Anoop, VS, Asharaf, S and Deepak, P (2016) Unsupervised concept hierarchy learning: a topic modeling guided approach. In Venugopal KR, Buyya R, Patnaik LM, Shenoy PD, Iyengar SS and Raja KB (eds), Twelfth International Conference on Communication Networks, ICCN 2016 & Twelfth International Conference on Data Mining and Warehousing, ICDMW 2016 & Twelfth International Conference on Image and Signal Processing, ICISP’2016, Vol. 89. The Netherlands: Elsevier Science, pp. 386–394.10.1016/j.procs.2016.06.086CrossRefGoogle Scholar
Baccianella, S, Esuli, A and Sebastiani, F (2010) SENTIWORDNET 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In 7th International Conference on Language Resource and Evaluation, LREC’2010, Valletta, Malta, May 17–23.Google Scholar
Bagheri, A, Saraee, M and de Jong, F (2013) Care more about customers: unsupervised domain-independent aspect detection for sentiment analysis of customer reviews. Knowledge-Based Systems 52, 201–213.10.1016/j.knosys.2013.08.011CrossRefGoogle Scholar
Balazs, JA and Velasquez, JD (2016) Opinion mining and information fusion: a survey. Information Fusion 27, 95–110.10.1016/j.inffus.2015.06.002CrossRefGoogle Scholar
Bo, P and Lee, L (2008) Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2, 1–135.Google Scholar
Cali, S and Balaman, SY (2019) Improved decisions for marketing, supply and purchasing: mining big data through an integration of sentiment analysis and intuitionistic fuzzy multi criteria assessment. Computers & Industrial Engineering 129, 315–332.10.1016/j.cie.2019.01.051CrossRefGoogle Scholar
Chen, C-C and Chuang, M-C (2008) Integrating the Kano model into a robust design approach to enhance customer satisfaction with product design. International Journal of Production Economics 114, 667–681.CrossRefGoogle Scholar
Chen, T, Xu, R, He, Y and Wang, X (2017) Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN. Expert Systems with Applications 72, 221–230.10.1016/j.eswa.2016.10.065CrossRefGoogle Scholar
Choi, J, Oh, S, Yoon, J, Lee, JM and Coh, BY (2020) Identification of time-evolving product opportunities via social media mining. Technological Forecasting and Social Change 156, 120045.10.1016/j.techfore.2020.120045CrossRefGoogle Scholar
Church, KW and Hanks, P (1990) Word association norms, mutual information, and lexicography. Computational Linguistics 16, 22–29.Google Scholar
del Pilar Salas-Zarate, M, Valencia-Garcia, R, Ruiz-Martinez, A and Colomo-Palacios, R (2017) Feature-based opinion mining in financial news: an ontology-driven approach. Journal of Information Science 43, 458–479.10.1177/0165551516645528CrossRefGoogle Scholar
Eirinaki, M, Pisal, S and Singh, J (2012) Feature-based opinion mining and ranking. Journal of Computer and System Sciences 78, 1175–1184.10.1016/j.jcss.2011.10.007CrossRefGoogle Scholar
Gruber, TR (1993) A translation approach to portable ontology specifications. Knowledge Acquisition 5, 199–220.10.1006/knac.1993.1008CrossRefGoogle Scholar
Guen, KS and Juyoung, K (2018) Analyzing the discriminative attributes of products using text mining focused on cosmetic reviews. Information Processing & Management 54, 938–957.Google Scholar
Han, SH, Kim, KJ, Yun, MH, Hong, SW and Kim, J (2004) Identifying mobile phone design features critical to user satisfaction. Human Factors and Ergonomics in Manufacturing 14, 15–29.10.1002/hfm.10051CrossRefGoogle Scholar
Hou, T, Yannou, B, Leroy, Y and Poirson, E (2019) Mining customer product reviews for product development: a summarization process. Expert Systems with Applications 132, 141–150.10.1016/j.eswa.2019.04.069CrossRefGoogle Scholar
Hu, MQ and Liu, B (2004) Mining opinion features in customer reviews. In Proceeding of the 19th National Conference on Artificial Intelligence and the 16th Conference on Innovative Applications of Artificial Intelligence. USA: AAAI, pp. 755–760.Google Scholar
Jeong, B, Yoon, J and Lee, J-M (2019) Social media mining for product planning: a product opportunity mining approach based on topic modeling and sentiment analysis. International Journal of Information Management 48, 280–290.10.1016/j.ijinfomgt.2017.09.009CrossRefGoogle Scholar
Jin, J, Ji, P and Gu, R (2016) Identifying comparative customer requirements from product online reviews for competitor analysis. Engineering Applications of Artificial Intelligence 49, 61–73.10.1016/j.engappai.2015.12.005CrossRefGoogle Scholar
Jin, J, Liu, Y, Ji, P and Kwong, CK (2019) Review on recent advances in information mining from big consumer opinion data for product design. Journal of Computing and Information Science in Engineering 19, 19.10.1115/1.4041087CrossRefGoogle Scholar
Joachims, T (1998) Text categorization with support vector machines: learning with many relevant features. In Nedellec C and Rouveirol C (eds), Machine Learning: 10th European Conference on Machine Learning. Proceedings, ECML’98, pp. 137–142. Germany: Springer-Verlag.10.1007/BFb0026683CrossRefGoogle Scholar
Ko, N, Jeong, B, Choi, S and Yoon, J (2018) Identifying product opportunities using social media mining: application of topic modeling and chance discovery theory. IEEE Access 6, 1680–1693.10.1109/ACCESS.2017.2780046CrossRefGoogle Scholar
Lau, RYK, Song, D, Li, Y, Cheung, TCH and Hao, J-X (2009) Toward a fuzzy domain ontology extraction method for adaptive e-learning. IEEE Transactions on Knowledge and Data Engineering 21, 800–813.10.1109/TKDE.2008.137CrossRefGoogle Scholar
Lau, RYK, Li, CP and Liao, SSY (2014) Social analytics: learning fuzzy product ontologies for aspect-oriented sentiment analysis. Decision Support Systems 65, 80–94.10.1016/j.dss.2014.05.005CrossRefGoogle Scholar
Li, SG and Li, YM (2019) A sentiment analysis of online reviews based on the word alignment model: a product improvement perspective. In Xu B (ed.), 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference. Proceedings, IMCEC’2018. USA: IEEE, pp. 2226–2231.Google Scholar
Liu, Q, Gao, ZQ, Liu, B and Zhang, YL (2015) Automated rule selection for aspect extraction in opinion mining. In Yang Q and Wooldridge M (eds), Proceedings of the 24th International Joint Conference on Artificial Intelligence, IJCAI. Germany: IJCAI, pp. 1291–1297.Google Scholar
Liu, C, Tang, L and Shan, W (2018) An extended HITS algorithm on bipartite network for features extraction of online customer reviews. Sustainability 10, 1425.Google Scholar
Miller, GA (1995) WordNet: a lexical database for English. Communications of the ACM 38, 39–41.10.1145/219717.219748CrossRefGoogle Scholar
Mirtalaie, MA, Hussain, OK, Chang, E and Hussain, FK (2018) Sentiment analysis of specific product's features using product tree for application in new product development. In Barolli L, Woungang I, and Hussain OK (eds), Advances in Intelligent Networking and Collaborative Systems, INCOS’2017, Vol. 8. Switzerland: Springer International Publishing AG, pp. 82–95.Google Scholar
Mirtalaie, MA, Hussain, OK, Chang, E and Hussain, FK (2019) A fine-grained ontology-based sentiment aggregation approach. In Barolli L, Javaid N, Ikeda M, and Takizawa M (eds), Complex, Intelligent, and Software Intensive Systems, Vol. 772. Switzerland: Springer International Publishing AG, pp. 252–262.10.1007/978-3-319-93659-8_22CrossRefGoogle Scholar
Penalver-Martinez, I, Garcia-Sanchez, F, Valencia-Garcia, R, Angel Rodriguez-Garcia, M, Moreno, V, Fraga, A and Luis Sanchez-Cervantes, J (2014) Feature-based opinion mining through ontologies. Expert Systems with Applications 41, 5995–6008.10.1016/j.eswa.2014.03.022CrossRefGoogle Scholar
Pinegar, JS (2006) What customers want: using outcome-driven innovation to create breakthrough products and services. Journal of Product Innovation Management 23, 464–466.CrossRefGoogle Scholar
Poria, S, Cambria, E and Gelbukh, A (2016 a) Aspect extraction for opinion mining with a deep convolutional neural network. Knowledge-Based Systems 108, 42–49.10.1016/j.knosys.2016.06.009CrossRefGoogle Scholar
Poria, S, Chaturvedi, I, Cambria, E and Bisio, F (2016 b) Sentic LDA: improving on LDA with semantic similarity for aspect-based sentiment analysis. In 2016 International Joint Conference on Neural Networks, IJCNN. USA: IEEE, pp. 4465–4473.10.1109/IJCNN.2016.7727784CrossRefGoogle Scholar
Ravi, K and Ravi, V (2015) A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowledge-Based Systems 89, 14–46.10.1016/j.knosys.2015.06.015CrossRefGoogle Scholar
Serrano-Guerrero, J, Olivas, JA, Romero, FP and Herrera-Viedma, E (2015) Sentiment analysis: a review and comparative analysis of web services. Information Sciences 311, 18–38.CrossRefGoogle Scholar
Studer, R, Benjamins, VR and Fensel, D (1998) Knowledge engineering: principles and methods. Data & Knowledge Engineering 25, 161–197.10.1016/S0169-023X(97)00056-6CrossRefGoogle Scholar
Sun, Q, Niu, J, Yao, Z and Yan, H (2019) Exploring eWOM in online customer reviews: sentiment analysis at a fine-grained level. Engineering Applications of Artificial Intelligence 81, 68–78.CrossRefGoogle Scholar
Thelwall, M, Buckley, K, Paltoglou, G, Cai, D and Kappas, A (2010) Sentiment in short strength detection informal text. Journal of the American Society for Information Science and Technology 61, 2544–2558.10.1002/asi.21416CrossRefGoogle Scholar
Trappey, AJC, Trappey, CV, Fan, CY and Lee, IJY (2018) Consumer driven product technology function deployment using social media and patent mining. Advanced Engineering Informatics 36, 120–129.10.1016/j.aei.2018.03.004CrossRefGoogle Scholar
Wang, H and Wang, W (2014) Product weakness finder: an opinion-aware system through sentiment analysis. Industrial Management & Data Systems 114, 1301–1320.CrossRefGoogle Scholar
Wang, W, Wang, H and Song, Y (2017 a) Ranking product aspects through sentiment analysis of online reviews. Journal of Experimental & Theoretical Artificial Intelligence 29, 227–246.10.1080/0952813X.2015.1132270CrossRefGoogle Scholar
Wang, W, Xin, G, Wang, B, Huang, J and Liu, Y (2017 b) Sentiment information extraction of comparative sentences based on CRF model. Computer Science and Information Systems 14, 823–837.CrossRefGoogle Scholar
Wasserman, GS (1993) On how to prioritize design requirements during the QFD planning process. IIE Transactions 25, 59–65.10.1080/07408179308964291CrossRefGoogle Scholar
Wu, C and Zhang, D (2019) Ranking products with IF-based sentiment word framework and TODIM method. Kybernetes 48, 990–1010.CrossRefGoogle Scholar
Wu, F, Huang, Y and Yuan, Z (2017) Domain-specific sentiment classification via fusing sentiment knowledge from multiple sources. Information Fusion 35, 26–37.10.1016/j.inffus.2016.09.001CrossRefGoogle Scholar
Yang, B, Liu, Y, Liang, Y and Tang, M (2019) Exploiting user experience from online customer reviews for product design. International Journal of Information Management 46, 173–186.CrossRefGoogle Scholar
Zhang, D and Liu, Q (2016) Biosensors and bioelectronics on smartphone for portable biochemical detection. Biosensors & Bioelectronics 75, 273–284.10.1016/j.bios.2015.08.037CrossRefGoogle ScholarPubMed
Zhang, KP, Xie, YS, Yang, Y, Sun, AR, Liu, HC and Choudhary, A (2014) Incorporating conditional random fields and active learning to improve sentiment identification. Neural Networks 58, 60–67.CrossRefGoogle ScholarPubMed
Zhang, JM, Chen, DX and Lu, M (2018) Combining sentiment analysis with a fuzzy Kano model for product aspect preference recommendation. IEEE Access 6, 59163–59172.CrossRefGoogle Scholar
Zhang, L, Chu, X and Xue, D (2019) Identification of the to-be-improved product features based on online reviews for product redesign. International Journal of Production Research 57, 2464–2479.10.1080/00207543.2018.1521019CrossRefGoogle Scholar
Zhu, J, Wang, H, Zhu, M, Tsou, BK and Ma, M (2011) Aspect-based opinion polling from customer reviews. IEEE Transactions on Affective Computing 2, 37–49.Google Scholar