A framework for finding and summarizing product defects, and ranking helpful threads from online customer forums through machine learning

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 and the results of the tests show that our methods are very promising: in fact, in most cases, they outperformed the current state-of-the-art methods.

Name thesis:degree_name

Ph. D.

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Computer Science and Applications

Department dc:contributor.department

Computer Science

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2013

Author dc:creator

• Jiao, Jian

Chair dc:contributor.committeechair

• Fan, Weiguo Patrick

Committee members dc:contributor.committeemember

• Abrahams, Alan Samuel
• Zhang, Liqing
• Ramakrishnan, Naren
• Wang, Gang Alan
• Fox, Edward A.

• product defect detection
• product feature extraction
• summarization
• clustering
• learning to rank
• thread ranking

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

vt_gsexam:732

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/23159