Session: DTM-03: Computational Methods in Design

Paper Number: 144098

144098 - Semi-Automated Mining of Customer Reviews to Identify Design Problems of Cantilever-Style Bassinets 

Customer reviews posted online provide a massive dataset that can identify product defects.  Locating informative reviews of a specific product is straightforward. However, it can be challenging to extract information that informs product designers of potential defects and drives useful testing to investigate the potential defect.  Such data extract can be manually performed in a reliable and effective manner by skilled engineers.  However, such an approach can be time-consuming and expensive.  On the other hand, completely automated data extraction would likely yield generic results that are not particularly useful.  This paper describes a semi-automated process wherein researchers develop an informed search algorithm that is based on an initial engineering assessment of a product that identifies potential design problems and words sets that are associated with the potential defects.  An automated search process then operates on the review database to greatly streamline the data analysis process.  The product reviews that indicate product defects are grouped together and associated with design features. The design features that are linked to the largest classes of complaints are then rigorously tested to evaluate the credibility and accuracy of the online complaints. The process is illustrated via a case study and testing of a cantilever-style bassinet.

Presenting Author: William Singhose Georgia Tech

Presenting Author Biography: William Singhose received a Ph.D. from the Massachusetts Institute of Technology in June 1997. He then joined the faculty of the Woodruff School of Mechanical Engineering at the Georgia Institute of Technology. In addition to his academic experience, Dr. Singhose has worked as a full-time engineer at Walt Disney World, Apple Computers, Convolve, and Google. He has developed and installed control systems on industrial machines such as silicon-handling robots, coordinate measuring machines, high precision air bearing positioning stages, and cranes. He also co-founded CAMotion Cranes and InVekTek, which have commercialized several of his technological developments. Control systems developed by these two companies have been deployed on hundreds of cranes at numerous automotive and primary metals manufacturing facilities around the world. His research has resulted in 6 issued patents and approximately 400 technical papers. Dr. Singhose has held visiting appointments at MIT, Stanford, the Tokyo Institute of Technology, and the Polytechnic University of Madrid.

Authors:

William Singhose Georgia Tech
Christopher Adams Georgia Tech
Rebecca Martinez Georgia Tech
Anjnee Rana Georgia Tech
Dooroo Kim Georgia Tech
Wayne Li Georgia Tech