Journal article
Vehicle defect discovery from social media
Decision Support Systems, Vol.54(1), pp.87-97
12/2012
DOI: 10.1016/j.dss.2012.04.005
Abstract
A pressing need of vehicle quality management professionals is decision support for the vehicle defect discovery and classification process. In this paper, we employ text mining on a popular social medium used by vehicle enthusiasts: online discussion forums. We find that sentiment analysis, a conventional technique for consumer complaint detection, is insufficient for finding, categorizing, and prioritizing vehicle defects discussed in online forums, and we describe and evaluate a new process and decision support system for automotive defect identification and prioritization. Our findings provide managerial insights into how social media analytics can improve automotive quality management.
► Online auto enthusiast forums contain many postings relating to vehicle defects. ► Therefore, social media analytics for vehicle quality management should be explored. ► We find that sentiment analysis is not effective for identifying vehicle defects. ► We propose a novel Vehicle Defect Discovery System (VDDS) using text mining. ► Results show robust defect classification across multiple vehicle brands.
Details
- Title: Subtitle
- Vehicle defect discovery from social media
- Creators
- Alan S Abrahams - Department of Business Information Technology, Pamplin College of Business, Virginia Tech, 1007 Pamplin Hall, Blacksburg, VA 24061, United StatesJian Jiao - Department of Computer Science, Virginia Tech, 114 McBryde Hall, Blacksburg, VA 24061, United StatesG. Alan Wang - Department of Business Information Technology, Pamplin College of Business, Virginia Tech, 1007 Pamplin Hall, Blacksburg, VA 24061, United StatesWeiguo Fan - Department of Accounting and Information Systems, Pamplin College of Business, Virginia Tech, 3007 Pamplin Hall, Blacksburg, VA 24061, United States
- Resource Type
- Journal article
- Publication Details
- Decision Support Systems, Vol.54(1), pp.87-97
- Publisher
- Elsevier B.V
- DOI
- 10.1016/j.dss.2012.04.005
- ISSN
- 0167-9236
- eISSN
- 1873-5797
- Language
- English
- Date published
- 12/2012
- Academic Unit
- Business Analytics
- Record Identifier
- 9984083214102771
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