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Extracting Consumer Preference From User-Generated Content Sources Using Classification

[+] Author Affiliations
Thomas Stone, Seung-Kyum Choi

Georgia Institute of Technology, Atlanta, GA

Paper No. DETC2013-13228, pp. V03AT03A031; 9 pages
  • ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 3A: 39th Design Automation Conference
  • Portland, Oregon, USA, August 4–7, 2013
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5588-1
  • Copyright © 2013 by ASME


The use of online, user-generated content for consumer preference modeling has been a recent topic of interest among the engineering and marketing communities. With the rapid growth of many different types of user-generate content sources, the tasks of reliable opinion extraction and data interpretation are critical challenges. This research investigates one of the largest and most-active content sources, Twitter, and its viability as a content source for preference modeling. Support Vector Machine (SVM) is used for sentiment classification of the messages, and a Twitter query strategy is developed to categorize messages according to product attributes and attribute levels. Over 7,000 messages are collected for a smartphone design case study. The preference modeling results are compared with those from a typical product review study, including over 2,500 product reviews. Overall, the results demonstrate that consumers do express their product opinions through Twitter; thus, this content source could potentially facilitate product design and decision-making via preference modeling.

Copyright © 2013 by ASME



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