Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6948403 | Decision Support Systems | 2018 | 41 Pages |
Abstract
As social media are continually gaining more popularity, they have become an important source for manufacturers to collect information related to defects on their products from consumers. Researchers have started to develop automated models to identify mentions of product defects from social media, such as online discussion forums. In this paper, we propose a novel method for product defect identification from online forums, addressing two inadequacies in previous studies, namely, the inadequate use of information contained in replies and the straightforward use of standard single classifier methods. Our method incorporates contextual features derived from replies and uses a multi-view ensemble learning method specifically tailored to the problem on hand. A case study in the automotive industry demonstrates the utilities of both novelties in our method.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Information Systems
Authors
Yao Liu, Cuiqing Jiang, Huimin Zhao,