Accuracy and Interpretability Testing of Text Mining Methods

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Extracting meaningful information from large collections of text data is problematic because of the sheer size of the database. However, automated analytic methods capable of processing such data have emerged. These methods, collectively called text mining first began to appear in 1988. A number of additional text mining methods quickly developed in independent research silos with each based on unique mathematical algorithms. How good each of these methods are at analyzing text is unclear. Method development typically evolves from some research silo centric requirement with the success of the method measured by a custom requirement-based metric. Results of the new ... continued below

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Ashton, Triss A. August 2013.

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This dissertation is part of the collection entitled: UNT Theses and Dissertations and was provided by UNT Libraries to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 388 times , with 11 in the last month . More information about this dissertation can be viewed below.

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  • Ashton, Triss A.

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Description

Extracting meaningful information from large collections of text data is problematic because of the sheer size of the database. However, automated analytic methods capable of processing such data have emerged. These methods, collectively called text mining first began to appear in 1988. A number of additional text mining methods quickly developed in independent research silos with each based on unique mathematical algorithms. How good each of these methods are at analyzing text is unclear. Method development typically evolves from some research silo centric requirement with the success of the method measured by a custom requirement-based metric. Results of the new method are then compared to another method that was similarly developed. The proposed research introduces an experimentally designed testing method to text mining that eliminates research silo bias and simultaneously evaluates methods from all of the major context-region text mining method families. The proposed research method follows a random block factorial design with two treatments consisting of three and five levels (RBF-35) with repeated measures. Contribution of the research is threefold. First, the users perceived a difference in the effectiveness of the various methods. Second, while still not clear, there are characteristics with in the text collection that affect the algorithms ability to extract meaningful results. Third, this research develops an experimental design process for testing the algorithms that is adaptable into other areas of software development and algorithm testing. This design eliminates the bias based practices historically employed by algorithm developers.

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UNT Theses and Dissertations

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  • August 2013

Added to The UNT Digital Library

  • April 23, 2014, 8:20 p.m.

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  • Nov. 16, 2016, 12:30 p.m.

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Ashton, Triss A. Accuracy and Interpretability Testing of Text Mining Methods, dissertation, August 2013; Denton, Texas. (digital.library.unt.edu/ark:/67531/metadc283791/: accessed April 22, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; .