Modelling customer satisfaction from online reviews using ensemble neural network and effect-based Kano model
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...
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2021
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/151229 |
| _version_ | 1826109441854930944 |
|---|---|
| author | Bi, Jian-Wu Liu, Yang Fan, Zhi-Ping Cambria, Erik |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Bi, Jian-Wu Liu, Yang Fan, Zhi-Ping Cambria, Erik |
| author_sort | Bi, Jian-Wu |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T02:18:17Z |
| format | Journal Article |
| id | ntu-10356/151229 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:18:17Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | ntu-10356/1512292021-06-17T03:00:28Z Modelling customer satisfaction from online reviews using ensemble neural network and effect-based Kano model Bi, Jian-Wu Liu, Yang Fan, Zhi-Ping Cambria, Erik School of Computer Science and Engineering Engineering::Computer science and engineering Customer Satisfaction Online Reviews 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. This work was partly supported by the National Natural Science Foundation of China [project numbers 71771043 and 71871049], Liaoning BaiQianWan Talents Program [project number 2016921027], the Fundamental Research Funds for the Central Universities, China [project number N170605001], and the 111 project [B16009]. 2021-06-17T03:00:28Z 2021-06-17T03:00:28Z 2019 Journal Article 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 0020-7543 0000-0003-2253-3492 0000-0002-5113-8638 0000-0001-6778-4637 https://hdl.handle.net/10356/151229 10.1080/00207543.2019.1574989 2-s2.0-85061183877 22 57 7068 7088 en International Journal of Production Research © 2019 Informa UK Limited, trading as Taylor & Francis Group. All rights reserved |
| spellingShingle | Engineering::Computer science and engineering Customer Satisfaction Online Reviews Bi, Jian-Wu Liu, Yang Fan, Zhi-Ping Cambria, Erik Modelling customer satisfaction from online reviews using ensemble neural network and effect-based Kano model |
| title | Modelling customer satisfaction from online reviews using ensemble neural network and effect-based Kano model |
| title_full | Modelling customer satisfaction from online reviews using ensemble neural network and effect-based Kano model |
| title_fullStr | Modelling customer satisfaction from online reviews using ensemble neural network and effect-based Kano model |
| title_full_unstemmed | Modelling customer satisfaction from online reviews using ensemble neural network and effect-based Kano model |
| title_short | Modelling customer satisfaction from online reviews using ensemble neural network and effect-based Kano model |
| title_sort | modelling customer satisfaction from online reviews using ensemble neural network and effect based kano model |
| topic | Engineering::Computer science and engineering Customer Satisfaction Online Reviews |
| url | https://hdl.handle.net/10356/151229 |
| work_keys_str_mv | AT bijianwu modellingcustomersatisfactionfromonlinereviewsusingensembleneuralnetworkandeffectbasedkanomodel AT liuyang modellingcustomersatisfactionfromonlinereviewsusingensembleneuralnetworkandeffectbasedkanomodel AT fanzhiping modellingcustomersatisfactionfromonlinereviewsusingensembleneuralnetworkandeffectbasedkanomodel AT cambriaerik modellingcustomersatisfactionfromonlinereviewsusingensembleneuralnetworkandeffectbasedkanomodel |