Quantitative quality control from qualitative data: control charts with latent semantic analysis

Quantitative quality control from qualitative data: control charts with latent semantic analysis Large quantities of data, often referred to as big data, are now held by companies. This big data includes statements of customer opinion regarding product or service quality in an unstructured textual form. While many tools exist to extract meaningful information from big data, automation tools do not exist to monitor the ongoing conceptual content of that data. We use latent semantic analysis to extract concept factors related to service quality categories. Customer comments found in the data that express dissatisfaction are then considered as representing a non-conforming observation in a process. Once factors are extracted, proportions of nonconformities for service quality failure categories are plotted on a control chart. The results are easily interpreted and the approach allows for the quantitative evaluation of customer acceptance of system process improvement initiatives. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality & Quantity Springer Journals

Quantitative quality control from qualitative data: control charts with latent semantic analysis

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Publisher
Springer Netherlands
Copyright
Copyright © 2014 by Springer Science+Business Media Dordrecht
Subject
Social Sciences, general; Methodology of the Social Sciences; Social Sciences, general
ISSN
0033-5177
eISSN
1573-7845
D.O.I.
10.1007/s11135-014-0036-5
Publisher site
See Article on Publisher Site

Abstract

Large quantities of data, often referred to as big data, are now held by companies. This big data includes statements of customer opinion regarding product or service quality in an unstructured textual form. While many tools exist to extract meaningful information from big data, automation tools do not exist to monitor the ongoing conceptual content of that data. We use latent semantic analysis to extract concept factors related to service quality categories. Customer comments found in the data that express dissatisfaction are then considered as representing a non-conforming observation in a process. Once factors are extracted, proportions of nonconformities for service quality failure categories are plotted on a control chart. The results are easily interpreted and the approach allows for the quantitative evaluation of customer acceptance of system process improvement initiatives.

Journal

Quality & QuantitySpringer Journals

Published: May 20, 2014

References

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