Conference proceeding
Discovering Product Defects and Solutions from Online User Generated Contents
WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), pp.3441-3447
01/01/2019
DOI: 10.1145/3308558.3313732
Abstract
The recent increase in online user generated content (UGC) has led to the availability of a large number of posts about products and services. Often, these posts contain complaints that the consumers purchasing the products and services have. However, discovering and summarizing product defects and the related knowledge from large quantities of user posts is a difficult task. Traditional aspect opinion mining models, that aim to discover the product aspects and their corresponding opinions, are not sufficient to discover the product defect information from the user posts. In this paper, we propose the Product Defect Latent Dirichlet Allocation model (PDLDA), a probabilistic model that identifies domain-specific knowledge about product issues using interdependent three-dimensional topics: Component, Symptom, and Resolution. A Gibbs sampling based inference method for PDLDA is also introduced. To evaluate our model, we introduce three novel product review datasets. Both qualitative and quantitative evaluations show that the proposed model results in apparent improvement in the quality of discovered product defect information. Our model has the potential to benefit customers, manufacturers, and policy makers, by automatically discovering product defects from online data.
Details
- Title: Subtitle
- Discovering Product Defects and Solutions from Online User Generated Contents
- Creators
- Xuan Zhang - Virginia TechZhilei Qiao - University of Alabama at BirminghamAman Ahuja - Virginia TechWeiguo Fan - University of IowaEdward A. Fox - Virginia TechChandan K. Reddy - Virginia Tech
- Resource Type
- Conference proceeding
- Publication Details
- WEB CONFERENCE 2019: PROCEEDINGS OF THE WORLD WIDE WEB CONFERENCE (WWW 2019), pp.3441-3447
- DOI
- 10.1145/3308558.3313732
- Publisher
- Assoc Computing Machinery
- Number of pages
- 7
- Grant note
- IIS-1619028; IIS-1707498; IIS-1838730 / US National Science Foundation; National Science Foundation (NSF) NVIDIA Corporation
- Language
- English
- Date published
- 01/01/2019
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380443702771
Metrics
18 Record Views