Volume 5, 2017: Issue 2

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Title:

Multivariate text mining for process improvement using cross-canonical correlation analysis

Author(s):

Jose Luis Guerrero Cusumano, Georgetown University, USA

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.

Keywords:

Text analysis, Google correlate, multivariate time series, cross correlation, canonical correlation, Radic matrices and determinant, predictive modelling

DOI:

https://doi.org/10.36965/OJAKM.2017.5(2)45-60

Type:

Research paper

Journal:

The Online Journal of Applied Knowledge Management (OJAKM), ISSN: 2325-4688

Publisher:

International Institute for Applied Knowledge Management (IIAKM)

Accepted:

20 May 2017

Pages:

45-60