Details

Autor(en) / Beteiligte

• Liu, Yao
• Jiang, Cuiqing
• Zhao, Huimin

Titel

Using contextual features and multi-view ensemble learning in product defect identification from online discussion forums

Ist Teil von

• Decision Support Systems, 2018-01, Vol.105, p.1-12

Ort / Verlag

Amsterdam: Elsevier B.V

Erscheinungsjahr

2018

Quelle

Access via ScienceDirect (Elsevier)

Beschreibungen/Notizen

• 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. •We propose novel contextual features for product defect identification from online forums.•We propose a multi-view ensemble learning method specifically for product defect identification.•We have applied and evaluated our proposed method in a case study in the automotive industry.