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ERIC Number: ED557859
Record Type: Non-Journal
Publication Date: 2013
Pages: 108
Abstractor: As Provided
Reference Count: N/A
ISBN: 978-1-3037-8769-0
ISSN: N/A
A Framework for Finding and Summarizing Product Defects, and Ranking Helpful Threads from Online Customer Forums through Machine Learning
Jiao, Jian
ProQuest LLC, Ph.D. Dissertation, Virginia Polytechnic Institute and State University.
The Internet has revolutionized the way users share and acquire knowledge. As important and popular Web-based applications, online discussion forums provide interactive platforms for users to exchange information and report problems. With the rapid growth of social networks and an ever increasing number of Internet users, online forums have accumulated a huge amount of valuable user-generated data and have accordingly become a major information source for business intelligence. This study focuses specifically on product defects, which are one of the central concerns of manufacturing companies and service providers, and proposes a machine learning method to automatically detect product defects in the context of online forums. To complement the detection of product defects, we also present a product feature extraction method to summarize defect threads and a thread ranking method to search for troubleshooting solutions. To this end, we collected different data sets to test these methods experimentally. The results of the tests show that our methods are very promising. In fact, in most cases, they outperformed current state-of-the-art methods. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]
ProQuest LLC. 789 East Eisenhower Parkway, P.O. Box 1346, Ann Arbor, MI 48106. Tel: 800-521-0600; Web site: http://www.proquest.com/en-US/products/dissertations/individuals.shtml
Publication Type: Dissertations/Theses - Doctoral Dissertations
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A