What's buzzing in the blizzard of buzz? Automotive component isolation in social media postings

  • Authors:

  • Alan S. Abrahams;Jian Jiao;Weiguo Fan;G. Alan Wang;Zhongju Zhang

  • Affiliations:

  • Department of Business Information Technology, Pamplin College of Business, Virginia Tech, 1007 Pamplin Hall, Blacksburg, VA 24061, United States;Department of Computer Science, Virginia Tech, 114 McBryde Hall, Blacksburg, VA 24061, United States;Department of Accounting and Information Systems, Pamplin College of Business, Virginia Tech, 3007 Pamplin Hall, Blacksburg, VA 24061, United States and School of Information, Zhejiang University ...;Department of Business Information Technology, Pamplin College of Business, Virginia Tech, 1007 Pamplin Hall, Blacksburg, VA 24061, United States;Operations and Information Management Department, School of Business, University of Connecticut, 2100 Hillside Road, Unit 1041, Storrs, CT 06269, United States

  • Venue:
  • Decision Support Systems
  • Year:
  • 2013

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Abstract

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.