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Automatic Identification of Product Usage Contexts from Online Customer Reviews

Published online by Cambridge University Press:  26 July 2019

Abstract

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There are three product design contexts that may significantly affect the design of a product and customer preferences towards product attributes, i.e. customer context, market context, and usage context factors. The conventional methods to gather product usage contexts may be costly and time consuming to conduct. As an alternative, this paper aims to automatically identify product usage contexts from publicly available online customer reviews. The proposed methodology consists of Preprocessing, Word Embedding, and Usage Context Clustering stages. The methodology is applied to identify usage contexts from laptop customer reviews, which results in 16 clusters of usage contexts. Furthermore, analyzing the review sentences explains the separation of “playing games” –which is more related to casual gaming, and “gaming rig” –which implies high computing power requirements. Finally, comparing customer review with manufacturer's product description may reveal a discrepancy to be investigated further by product designer, e.g. a customer suggests a laptop for basic use, although the manufacturer's description describes it for heavy use.

Type
Article
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) 2019

References

Banerjee, A., Dhillon, I. S., Ghosh, J. and Sra, S. (2005), “Clustering on the Unit Hypersphere Using Von Mises-Fisher Distributions”, Journal of Machine Learning Research, Vol. 6 pp. 13451382.Google Scholar
Belk, R. (1975), “Situational Variables and Consumer Behavior”. Journal of Consumer Research, Vol. 2 No. 3, pp. 157164.Google Scholar
Decker, R. and Trusov, M. (2010), “Estimating Aggregate Consumer Preferences from Online Product Reviews”, International Journal of Research in Marketing, Vol. 27 No. 4, pp. 293307.Google Scholar
Green, M. G., Palani, R. P. K. and Wood, K. L. (2004), “Product Usage Context: Improving Customer Needs Gathering and Design Target Setting”, ASME 2004 Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Salt Lake City, UT, USA.Google Scholar
Green, M. G., Tan, J., Linsey, J. S., Seepersad, C. C. and Wood, K. L. (2005), “Effects of Product Usage Context on Consumer Product Preferences”, ASME 2005 Design Engineering Technical Conferences & Computers and Information in Engineering Conference, Long Beach, CA, USA.Google Scholar
Green, M. G., Linsey, J. S., Seepersad, C. C. and Wood, K. L. (2006), “Frontier Design: A Product Usage Context Method”, ASME 2006 Design Engineering Technical Conferences & Computers and Information in Engineering Conference, Philadelphia, PA, USA.Google Scholar
He, L., Chen, W., Hoyle, C. and Yannou, B. (2012), “Choice Modeling for Usage Context-Based Design”, Journal of Mechanical Design, Vol. 134 No. 3, p. 031007. https://doi.org/10.1115/1.4005860Google Scholar
LaFleur, R. S. (1992), “Principal Engineering Design Questions”. Research in Engineering Design, Vol. 4 No. 2, pp. 89100. https://doi.org/10.1007/BF01580147Google Scholar
Mikolov, T., Chen, K., Corrado, G. and Dean, J. (2013a). “Efficient Estimation of Word Representations in Vector Space”. CoRR, Vol. abs/1301.3781. http://arxiv.org/abs/1301.3781Google Scholar
Mikolov, T., Sutskever, I., Kai, C., Corrado, G. and Dean, J. (2013b), “Distributed Representations of Words and Phrases and Their Compositionality”, 26th International Conference on Neural Information Processing Systems - Volume 2, Curran Associates Inc., USA, pp. 31113119.Google Scholar
Novikov, A. (2018), annoviko/pyclustering: pyclustering 0.8.2 release. [online]. Available at: (https://github.com/annoviko/pyclustering) (November 21, 2018). https://doi.org/10.5281/zenodo.1491324.Google Scholar
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. and Duchesnay, E. (2011), “Scikit-learn: Machine Learning in Python”, Journal of Machine Learning Research, Vol. 12, pp. 28252830.Google Scholar
Pelleg, D. and Moore, A. W. (2000), “X-means: Extending K-means with Efficient Estimation of the Number of Clusters”, Seventeenth International Conference on Machine Learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp. 727734.Google Scholar
Řehůřek, R. and Sojka, P. (2011), “Software Framework for Topic Modelling with Large Corpora”, LREC 2010 Workshop on New Challenges for NLP Frameworks, ELRA, Valletta, Malta, pp. 4550.Google Scholar
Rong, X. (2014), “word2vec Parameter Learning Explained”. CoRR, Vol. abs/1411.2738. http://arxiv.org/abs/1411.2738Google Scholar
Ulrich, K. T. and Eppinger, S. D. (2004), Product Design and Development, McGraw-Hill/Irwin, Boston, MA, USA.Google Scholar
Xue, W. and Li, T. (2018), “Aspect Based Sentiment Analysis with Gated Convolutional Networks”, 56th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Melbourne, Australia, pp. 25142523.Google Scholar
Zhou, F., Jiao, R. J. and Linsey, J. S. (2015), “Latent Customer Needs Elicitation by Use Case Analogical Reasoning from Sentiment Analysis of Online Product Reviews”, Journal of Mechanical Design, Vol. 137 No. 7, p. 071401. https://doi.org/10.1115/1.4030159Google Scholar