Title:
A Text Mining Framework Linking Technical Intelligence from Publication Databases to Strategic Technology Decisions
A Text Mining Framework Linking Technical Intelligence from Publication Databases to Strategic Technology Decisions
Author(s)
Courseault, Cherie Renee
Advisor(s)
Porter, Alan L.
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Abstract
This research developed a comprehensive methodology to quickly monitor key technical intelligence areas, provided a method that cleanses and consolidates information into an understandable, concise picture of topics of interest, thus bridging issues of managing technology and text mining. This research evaluated and altered some existing analysis methods, and developed an overall framework for answering technical intelligence questions. A six-step approach worked through the various stages of the Intelligence and Text Data Mining Processes to address issues that hindered the use of Text Data Mining in the Intelligence Cycle and the actual use of that intelligence in making technology decisions. A questionnaire given to 34 respondents from four different industries identified the information most important to decision-makers as well as clusters of common interests. A bibliometric/text mining tool applied to journal publication databases, profiled technology trends and presented that information in the context of the stated needs from the questionnaire.
In addition to identifying the information that is important to decision-makers, this research improved the methods for analyzing information. An algorithm was developed that removed common non-technical terms and delivered at least an 89% precision rate in identifying synonymous terms. Such identifications are important to improving accuracy when mining free text, thus enabling the provision of the more specific information desired by the decision-makers. This level of precision was consistent across five different technology areas and three different databases. The result is the ability to use abstract phrases in analysis, which allows the more detailed nature of abstracts to be captured in clustering, while portraying the broad relationships as well.
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Date Issued
2004-04-12
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3165197 bytes
Resource Type
Text
Resource Subtype
Dissertation