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WHAT USERS WANT: A NATURAL LANGUAGE PROCESSING APPROACH TO DISCOVER USERS' NEEDS FROM ONLINE REVIEWS

Published online by Cambridge University Press:  19 June 2023

Irene Spada ,
Simone Barandoni ,
Vito Giordano ,
Filippo Chiarello ,
Gualtiero Fantoni  and
Antonella Martini
Irene Spada*
Affiliation:
School of Engineering, Department of Information Engineering, University of Pisa, Italy; B4DS - Business Engineering for Data Science lab, University of Pisa, Italy
Simone Barandoni
Affiliation:
Department of Computer Science, University of Pisa, Italy; B4DS - Business Engineering for Data Science lab, University of Pisa, Italy
Vito Giordano
Affiliation:
School of Engineering, Department of Information Engineering, University of Pisa, Italy; B4DS - Business Engineering for Data Science lab, University of Pisa, Italy
Filippo Chiarello
Affiliation:
School of Engineering, Department of Energy, Systems, Land and Construction Engineering, University of Pisa, Italy; B4DS - Business Engineering for Data Science lab, University of Pisa, Italy
Gualtiero Fantoni
Affiliation:
School of Engineering, Department of Civil and Industrial Engineering, University of Pisa, Italy; B4DS - Business Engineering for Data Science lab, University of Pisa, Italy
Antonella Martini
Affiliation:
School of Engineering, Department of Energy, Systems, Land and Construction Engineering, University of Pisa, Italy; B4DS - Business Engineering for Data Science lab, University of Pisa, Italy
*
Spada, Irene, University of Pisa, Italy, irene.spada@phd.unipi.it

Abstract

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Digital media are a means to deliver products and services, but also a channel to interact with consumers and a source of information on users’ preferences. Data shared by customers on the web, the User-Generated Content (UGC), can give entrepreneurs a detailed perspective of the market. This work examines an application of Natural Language Processing techniques on UGC to discover insights on users' opinions. We collected more than 13.000 reviews of software from digital stores and review website to gather information on the customers’ perspective and their response to a given marketing strategy in two case studies on digital product's launch. The objective is to give support to two Italian companies in the process of business model development through data-driven evidence. We aim to discover who are the users and which are their needs using a lexicon-based approach to mine unstructured text. The results provide qualitative and quantitative descriptions of the market segments. We propose a method to examine UGC and to explore customers’ behavior on social media. The findings helped managers for the development of their business model, enhancing an informed decision-making process.

Keywords

Semantic data processingUser centred designRequirements
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 3879 - 3888
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

References

Abrahams, A.S., Fan, W., Wang, G.A., Zhang, Z. and Jiao, J., (2015), “An integrated text analytic framework for product defect discovery.”, Production and Operations Management, Vol. 24 No. 6, pp. 975–990. https://doi.org/10.1111/poms.12303CrossRefGoogle Scholar
Adams, K.M., (2015), Nonfunctional requirements in systems analysis and design, Cham: Springer international publishing. https://doi.org/10.1007/978-3-319-18344-2_3CrossRefGoogle Scholar
Barravecchia, F., Mastrogiacomo, L., and Franceschini, F. (2021), “Digital voice-of-customer processing by topic modelling algorithms: insights to validate empirical results.”, International Journal of Quality & Reliability Management, Vol. 39 No. 6, pp.1453–1470. https://doi.org/10.1108/IJQRM-07-2021-0217CrossRefGoogle Scholar
Capterra (2022, January), Capterra Terms of Use, retrieved February 2023, from Capterra, Capterra General User Terms. Available at: https://www.capterra.com/legal/terms-of-useGoogle Scholar
Checchinato, F. (2021), “Digital transformation and consumer behaviour: How the analysis of consumer data reshapes the marketing approach.”, In: Hinterhuber, A., Vescovi, T., & Checchinato, F. (Eds.), Managing Digital Transformation: Understanding the Strategic Process., Routledge, London, pp. 165–176. https://doi.org/10.4324/9781003008637CrossRefGoogle Scholar
Chiarello, F., Cimino, A., Fantoni, G., and Dell'Orletta, F. (2018), “Automatic users extraction from patents.”, World Patent Information, Vol. 54, pp. 28–38. https://doi.org/10.1016/j.wpi.2018.07.006CrossRefGoogle Scholar
Chiarello, F., Belingheri, P., and Fantoni, G. (2021), “Data science for engineering design: State of the art and future directions.”, Computers in Industry, Vol. 129, p. 103447. https://doi.org/10.1016/j.compind.2021.103447CrossRefGoogle Scholar
DeAndrea, D. C., Van Der Heide, B., Vendemia, M. A., and Vang, M. H. (2018), “How people evaluate online reviews.”, Communication Research, Vol. 45 No. 5, pp. . https://doi.org/10.1177/0093650215573862CrossRefGoogle Scholar
De Luca, L. M., Herhausen, D., Troilo, G., and Rossi, A. (2021), “How and when do big data investments pay off? The role of marketing affordances and service innovation.”, Journal of the Academy of Marketing Science, Vol. 49 No. 4, pp. 790–810. https://doi.org/10.1007/s11747-020-00739-xCrossRefGoogle Scholar
Ding, X., Liu, B., and Yu, P. S. (2008), “A holistic lexicon-based approach to opinion mining.”, Proceedings of the 2008 international conference on web search and data mining, pp. 231–240. https://doi.org/10.1145/1341531.1341561CrossRefGoogle Scholar
Du, X., Jiao, J., and Tseng, M. M. (2003), “Identifying customer need patterns for customization and personalization.”, Integrated manufacturing systems, Vol. 14 No. 5, pp. 387–396. https://doi.org/10.1108/09576060310477799CrossRefGoogle Scholar
Fontanarava, J., Pasi, G. and Viviani, M. (2017), “Feature analysis for fake review detection through supervised classification.”, 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), Tokyo, Japan, pp. 658–666. https://doi.org/10.1109/DSAA.2017.51CrossRefGoogle Scholar
Harding, J. A., Popplewell, K., Fung, R. Y., and Omar, A. R. (2001), “An intelligent information framework relating customer requirements and product characteristics.”, Computers in Industry, Vol. 44 No. 1, pp. 51–65. https://doi.org/10.1016/S0166-3615(00)00074-9CrossRefGoogle Scholar
Jain, P. K., Pamula, R., and Ansari, S. (2021), “A supervised machine learning approach for the credibility assessment of user-generated content.”, Wireless Personal Communications, Vol. 118 No. 4, pp. 2469–2485. https://doi.org/10.1007/s11277-021-08136-5CrossRefGoogle Scholar
Johann, T., Stanik, C., and Maalej, W. (2017), “Safe: A simple approach for feature extraction from app descriptions and app reviews.”, 2017 IEEE 25th international requirements engineering conference (RE), Lisbon, Portugal, 2017, pp. 21–30. https://doi.org/10.1109/RE.2017.71CrossRefGoogle Scholar
Kauffmann, E., Peral, J., Gil, D., Ferrández, A., Sellers, R., and Mora, H. (2020), “A framework for big data analytics in commercial social networks: A case study on sentiment analysis and fake review detection for marketing decision-making.”, Industrial Marketing Management, Vol. 90, pp. 523–537. https://doi.org/10.1016/j.indmarman.2019.08.003CrossRefGoogle Scholar
Kiecker, P., and Cowles, D. (2002), “Interpersonal communication and personal influence on the Internet: A framework for examining online word-of-mouth.”, Journal of Euromarketing, Vol. 11 No. 2, pp. 71–88. https://doi.org/10.1300/J037v11n02_04CrossRefGoogle Scholar
Krumm, J., Davies, N., and Narayanaswami, C. (2008), “User-generated content.”, IEEE Pervasive Computing, Vol. 7 No. 4, pp. 10–11. https://doi.org/10.1109/MPRV.2008.85CrossRefGoogle Scholar
Kühl, N., Mühlthaler, M., and Goutier, M. (2020), “Supporting customer-oriented marketing with artificial intelligence: automatically quantifying customer needs from social media.”, Electronic Markets, Vol. 30 No. 2, pp. 351–367. https://doi.org/10.1007/s12525-019-00351-0CrossRefGoogle Scholar
Kurtanović, Z., and Maalej, W. (2017), “Mining user rationale from software reviews.”, 2017 IEEE 25th international requirements engineering conference (RE), Lisbon, Portugal, 2017, pp. 61–70. https://doi.org/10.1109/RE.2017.86CrossRefGoogle Scholar
Li, F. H., Huang, M., Yang, Y., and Zhu, X. (2011), “Learning to identify review spam.”, Twenty-second international joint conference on artificial intelligence, Barcelona, Spain, 2011. https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-414CrossRefGoogle Scholar
Li, M. F., Zhang, G. X., Zhao, L. T., and Song, T. (2022), “Extracting product competitiveness through user-generated content: A hybrid probabilistic inference model.”, Journal of King Saud University-Computer and Information Sciences, Vol 34 No. 6, pp. 2720–2732. https://doi.org/10.1016/j.jksuci.2022.03.018CrossRefGoogle Scholar
Öberg, C., and Alexander, A. T. (2019), “The openness of open innovation in ecosystems–Integrating innovation and management literature on knowledge linkages.”, Journal of Innovation & Knowledge, Vol. 4 No. 4, pp. 211–218. https://doi.org/10.1016/j.jik.2017.10.005CrossRefGoogle Scholar
O'Reilly, K., MacMillan, A., Mumuni, A. G., and Lancendorfer, K. M. (2016), “Extending our understanding of eWOM impact: The role of source credibility and message relevance.”, Journal of Internet Commerce, Vol. 15 No. 2, pp. 77–96. https://doi.org/10.1080/15332861.2016.1143215CrossRefGoogle Scholar
Pan, Y., and Zhang, J. Q. (2011), “Born unequal: a study of the helpfulness of user-generated product reviews.”, Journal of retailing, Vol. 87 No. 4, pp. 598–612. https://doi.org/10.1016/j.jretai.2011.05.002CrossRefGoogle Scholar
Park, S., Joung, J., and Kim, H. (2023), “Spec guidance for engineering design based on data mining and neural networks.”, Computers in Industry, Vol. 144, p. 103790. https://doi.org/10.1016/j.compind.2022.103790CrossRefGoogle Scholar
Pawar, S., Srivastava, R., and Palshikar, G. K. (2012, January), “Automatic gazette creation for named entity recognition and application to resume processing.”, Proceedings of the 5th ACM COMPUTE Conference: Intelligent & scalable system technologies, pp. 1–7. https://doi.org/10.1145/2459118.2459133CrossRefGoogle Scholar
Saura, J. R., Ribeiro-Soriano, D., and Palacios-Marqués, D. (2021), “From user-generated data to data-driven innovation: A research agenda to understand user privacy in digital markets.”, International Journal of Information Management, Vol. 60, p. 102331. https://doi.org/10.1016/j.ijinfomgt.2021.102331CrossRefGoogle Scholar
Tang, L., Li, J., Du, H., Li, L., Wu, J., and Wang, S. (2022), “Big Data in Forecasting Research: A Literature Review.”, Big Data Research, Vol. 27, p. 100289. https://doi.org/10.1016/j.bdr.2021.100289CrossRefGoogle Scholar
Vlačić, B., Corbo, L., e Silva, S. C., and Dabić, M. (2021), “The evolving role of artificial intelligence in marketing: A review and research agenda.”, Journal of Business Research, Vol. 128, pp. 187–203. https://doi.org/10.1016/j.jbusres.2021.01.055CrossRefGoogle Scholar
Wang, L., Youn, B. D., Azarm, S., and Kannan, P. K. (2011), “Customer-driven product design selection using web based user-generated content.”, International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Vol. 54822, pp. 405–419. https://doi.org/10.1115/DETC2011-48338CrossRefGoogle Scholar
Wang, Y., Luo, L., and Liu, H. (2020), “Bridging the semantic gap between customer needs and design specifications using user-generated content.”, IEEE Transactions on Engineering Management, Vol. 69, pp. 1622 – 1634. https://doi.org/10.1109/TEM.2020.3021698CrossRefGoogle Scholar
Zhang, J., Simeone, A., Gu, P., and Hong, B. (2018), “Product features characterization and customers’ preferences prediction based on purchasing data.”, CIRP Annals, Vol. 67 No. 1, pp. 149–152. https://doi.org/10.1016/j.cirp.2018.04.020CrossRefGoogle Scholar