Sunday, February 16, 2025
Volume 5, 2017: Issue 2 |
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Title: |
Multivariate text mining for process improvement using cross-canonical correlation analysis |
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Author(s): |
Jose Luis Guerrero Cusumano, Georgetown University, USA |
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Abstract: |
Text analysis is a useful tool to determine what a company and its customers want in order to improve processes and methodologies of analysis. Searches in databases may have a time series component that determines the importance and sequences of multivariate searches and its structure. This paper presents a methodology to simplify and model multivariate searches in time using the Canonical Correlation approach. The techniques shown provide a robust methodology to simplify the analysis and create predictive models taking into account temporal dependencies. |
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Keywords: |
Text analysis, Google correlate, multivariate time series, cross correlation, canonical correlation, Radic matrices and determinant, predictive modelling |
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DOI: |
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Type: |
Research paper |
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Journal: |
The Online Journal of Applied Knowledge Management (OJAKM), ISSN: 2325-4688 |
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Publisher: |
International Institute for Applied Knowledge Management (IIAKM) |
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Accepted: |
20 May 2017 |
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Pages: |
45-60 |