Explainable neural network-based approach to Kano categorisation of product features from online reviews

ABSTRACT

The Kano model is an extensively used technique for understanding different types of customer preferences. It classifies product features based on the effects of their performance on the overall customer satisfaction. Compared to surveys, numerous online reviews can be easily collected at a lower cost. This paper proposes an explainable neural network-based approach for the Kano categorisation of product features from online reviews. First, product feature words are identified by clustering nouns based on word embedding. Subsequently, the sentiments of the product feature words are determined by conducting the Vader sentiment analysis. Finally, the effects of the sentiments of each product feature on the star rating are estimated using explainable neural networks. Based on their effects, the product features are classified into the Kano categories. A case study of three Fitbit models is performed to validate the proposed approach. The Kano categorisation by the proposed approach is compared with the results of a previous product feature word clustering and ensemble neural network-based method. The results exhibit that the former presents a more reliable performance than the latter. The proposed approach is automated after providing several hyperparameters and can assist companies in conducting the Kano analysis with increased speed and efficiency.

KEYWORDS:

• Kano model
• customer preference
• product design
• artificial intelligence
• interpretable model

Data availability statement

Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data is not available.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 https://github.com/cjhutto/vaderSentiment

2 https://github.com/slundberg/shap

Additional information

Funding

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2019R1I1A1A01063298, 2021R1I1A1A01044552).

Notes on contributors

Junegak Joung

Junegak Joung is currently an assistant research professor in the Department of Industrial Engineering at Ulsan National Institute of Science and Technology (UNIST). He received a BS and a Ph.D. from the Department of Industrial and Management Engineering at Pohang University of Science and Technology (POSTECH) in 2013 and 2018. His main research interests include user data mining, interpretable machine learning applications, and data-driven product/service quality management. E-mail: [email protected]

Harrison M. Kim

Harrison Kim is currently a professor in the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign. He received BS (1995) and MS (1997) from KAIST and Ph.D. (2001) from the University of Michigan. His main research interests include user-centred sustainable product design; energy systems engineering; product design analytics; renewable energy and vehicle electrification; multi-scale, multidisciplinary optimization; green product portfolio design and manufacturing. E-mail: [email protected]