Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/151229
Title: Modelling customer satisfaction from online reviews using ensemble neural network and effect-based Kano model
Authors: Bi, Jian-Wu
Liu, Yang
Fan, Zhi-Ping
Cambria, Erik
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: Bi, J., Liu, Y., Fan, Z. & Cambria, E. (2019). Modelling customer satisfaction from online reviews using ensemble neural network and effect-based Kano model. International Journal of Production Research, 57(22), 7068-7088. https://dx.doi.org/10.1080/00207543.2019.1574989
Journal: International Journal of Production Research
Abstract: With the rapid advances in information technology, an increasing number of online reviews are posted daily on the Internet. Such reviews can serve as a promising data source to understand customer satisfaction. To this end, in this paper, we proposed a method for modelling customer satisfaction from online reviews. In the method, customer satisfaction dimensions (CSDs) are first extracted from online reviews based on latent dirichlet allocation (LDA). The sentiment orientations of the extracted CSDs are identified using a support vector machine (SVM). Then, considering the existence of complex relationships among different CSDs and the customer satisfaction, an ensemble neural network based model (ENNM) is proposed to measure the effects of customer sentiments toward different CSDs on customer satisfaction. On this basis, to identify the category of each CSD from the customer’s perspective, an effect-based Kano model (EKM) is proposed. Finally, an empirical study, which consists of two parts (phones and cameras), is given to illustrate the effectiveness of the proposed method.
URI: https://hdl.handle.net/10356/151229
ISSN: 0020-7543
DOI: 10.1080/00207543.2019.1574989
Rights: © 2019 Informa UK Limited, trading as Taylor & Francis Group. All rights reserved
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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