Nahar, Kamrun (2012) Classifying tablet PC models based on user preferences from online reviews. Masters thesis, Concordia University.
- Accepted Version
Online review sites are a good source of information for the manufacturers to understand the product market. Those sites allow users of the product to express their opinions about products which provide valuable information to other people. As these reviews are easily available and contain important information about the product and users, product designers can utilize those reviews for their new product design analysis. To be competitive the designer should consider the users preferences at the time of product designing and should offer product differentiation while offering a new product. Tablet PC is currently considered
as a new class of product which needs to be well classified for the users. The history of portable computer tells that at first when portable computer arrived in the market it was
also not well classified for different users. At first, almost all manufacturers had one line of portable computer in market which resembles the current time of tablet product. Motivated by the available online reviews by tablet users and the need of the tablet designers, we propose a method to extract interesting patterns from online reviews of tablet users. These extracted patterns can help the designers to understand the new product market of tablet PC to classify its model for different categories of users. We applied association rule mining technique on the online reviews to reveal interesting patterns between users and their preferred tablet features. For identifying this pattern we considered three categories of users:
personal, business and student users. To examine the approach, the online reviews posted between April, 2010 and May, 2011 were collected. Then the resultant association rules between the users and tablet features are compared with the existing tablets in the market which supports this study.
|Divisions:||Concordia University > Faculty of Engineering and Computer Science > Concordia Institute for Information Systems Engineering|
|Item Type:||Thesis (Masters)|
|Degree Name:||M.A. Sc.|
|Program:||Quality Systems Engineering|
|Date:||6 May 2012|
|Thesis Supervisor(s):||Fung, Benjamin and Li, Simon|
|Deposited By:||Kamrun Nahar|
|Deposited On:||25 Oct 2012 15:32|
|Last Modified:||25 Oct 2012 15:32|
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