Vehicle defect discovery from social media

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.