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

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.