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Proceedings Paper

Data modeling enabled dynamical analysis for blogger state-of-mind modeling and prediction
Author(s): Holger M. Jaenisch; Michael J. Coombs; James W. Handley; Nathaniel G. Albritton; Matthew E. Edwards
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Paper Abstract

We present a novel mathematical framework for Data Mining blogger text entries and converting latent conceptual information into analytical predictive equations. These differential equations are conceptual models of the blogger's topic and state-of-mind transition dynamics. The mathematical framework is explored for its value in characterization of topic content and topic tracking as well as identification and prediction of topic dynamic changes.

Paper Details

Date Published: 1 May 2008
PDF: 9 pages
Proc. SPIE 6964, Evolutionary and Bio-Inspired Computation: Theory and Applications II, 69640A (1 May 2008); doi: 10.1117/12.775565
Show Author Affiliations
Holger M. Jaenisch, Alabama A&M Univ. (United States)
Licht Strahl Engineering Inc. (United States)
Michael J. Coombs, Diplomacy Media Research (United States)
James W. Handley, Licht Strahl Engineering Inc. (United States)
Amtec Corp. (United States)
Nathaniel G. Albritton, Amtec Corp. (United States)
Matthew E. Edwards, Alabama A&M Univ. (United States)

Published in SPIE Proceedings Vol. 6964:
Evolutionary and Bio-Inspired Computation: Theory and Applications II
Misty Blowers; Alex F. Sisti, Editor(s)

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