TY - JOUR
T1 - What's buzzing in the blizzard of buzz? Automotive component isolation in social media postings
AU - Abrahams, Alan S.
AU - Jiao, Jian
AU - Fan, Weiguo
AU - Wang, G. Alan
AU - Zhang, Zhongju
N1 - Funding Information:
The authors are grateful to Professor Mehdi Ahmadian, Director of the Center for Vehicle Systems and Safety, Virginia Tech, for his helpful feedback, guidance, and assistance. This research is partly supported by the Natural Science Foundation of China (Grant #70872089 and #71072129 ) and the National Science Foundation (Grant #DUE-0840719 ).
PY - 2013/11
Y1 - 2013/11
N2 - In the blizzard of social media postings, isolating what is important to a corporation is a huge challenge. In the consumer-related manufacturing industry, for instance, manufacturers and distributors are faced with an unrelenting, accumulating snow of millions of discussion forum postings. In this paper, we describe and evaluate text mining tools for categorizing this user-generated content and distilling valuable intelligence frozen in the mound of postings. Using the automotive industry as an example, we implement and tune the parameters of a text-mining model for component diagnostics from social media. Our model can automatically and accurately isolate the vehicle component that is the subject of a user discussion. The procedure described also rapidly identifies the most distinctive terms for each component category, which provides further marketing and competitive intelligence to manufacturers, distributors, service centers, and suppliers.
AB - In the blizzard of social media postings, isolating what is important to a corporation is a huge challenge. In the consumer-related manufacturing industry, for instance, manufacturers and distributors are faced with an unrelenting, accumulating snow of millions of discussion forum postings. In this paper, we describe and evaluate text mining tools for categorizing this user-generated content and distilling valuable intelligence frozen in the mound of postings. Using the automotive industry as an example, we implement and tune the parameters of a text-mining model for component diagnostics from social media. Our model can automatically and accurately isolate the vehicle component that is the subject of a user discussion. The procedure described also rapidly identifies the most distinctive terms for each component category, which provides further marketing and competitive intelligence to manufacturers, distributors, service centers, and suppliers.
KW - Diagnostics
KW - Social media analytics
KW - Text mining
KW - User-generated content (UGC)
UR - http://www.scopus.com/inward/record.url?scp=84884210911&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84884210911&partnerID=8YFLogxK
U2 - 10.1016/j.dss.2012.12.023
DO - 10.1016/j.dss.2012.12.023
M3 - Article
AN - SCOPUS:84884210911
SN - 0167-9236
VL - 55
SP - 871
EP - 882
JO - Decision Support Systems
JF - Decision Support Systems
IS - 4
ER -