期刊
DECISION SUPPORT SYSTEMS
卷 55, 期 4, 页码 871-882出版社
ELSEVIER
DOI: 10.1016/j.dss.2012.12.023
关键词
Social media analytics; Diagnostics; Text mining; User-generated content (UGC)
类别
资金
- Natural Science Foundation of China [70872089, 71072129]
- National Science Foundation [DUE-0840719]
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. (c) 2012 Elsevier B.V. All rights reserved.
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