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| Content Provider | Springer Nature Link |
|---|---|
| Author | Li, Simon Nahar, Kamrun Fung, Benjamin C. M. |
| Copyright Year | 2013 |
| Abstract | Since the first release of Apple iPad in April 2010, tablet computers (or simply tablets) can be viewed as a new category of technological products for general consumers. To compete in the evolving market, it is expected that the manufacturers need to customize the design of tablets to satisfy different needs of customers. To support product customization in this context, one specific challenge is that the customer information of tablets is relatively limited due to the newness of the product category. To tackle this challenge, this paper proposes a data mining approach to analyze the online reviews that contain customer opinions towards tablet products. In this research, tablet usages are first classified according to three user types: personal, business and student. From the online reviews, association rule mining is applied to investigate what tablet attributes are desired by specific user types. To examine the approach, the online reviews were collected from the time between April 2010 (first release of Apple iPad) and May 2011. Then, the association rule results from that time are compared with the recent tablet products on the market. It is demonstrated that online reviews as the original customer information can provide relevant insights for product customization. |
| Starting Page | 97 |
| Ending Page | 110 |
| Page Count | 14 |
| File Format | |
| ISSN | 09565515 |
| Journal | Journal of Intelligent Manufacturing |
| Volume Number | 26 |
| Issue Number | 1 |
| e-ISSN | 15728145 |
| Language | English |
| Publisher | Springer US |
| Publisher Date | 2013-04-04 |
| Publisher Place | Boston |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Product customization Customer online reviews Association rule mining Production/Logistics/Supply Chain Manufacturing, Machines, Tools Control, Robotics, Mechatronics |
| Content Type | Text |
| Resource Type | Article |
| Subject | Industrial and Manufacturing Engineering Artificial Intelligence Software |
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