Catalog Home Page

Assisting reading and analysis of text documents by visualization

Maloney, Ross J. (2005) Assisting reading and analysis of text documents by visualization. PhD thesis, Murdoch University.

[img]
Preview
PDF - Front Pages
Download (163kB) | Preview
    [img]
    Preview
    PDF - Whole Thesis
    Download (3001kB) | Preview

      Abstract

      The research reported here examined the use of computer generated graphics as a means to assist humans to analyse text documents which have not been subject to markup. The approach taken was to survey available visualization techniques in a broad selection of disciplines including applications to text documents, group those techniques using a taxonomy proposed in this research, then develop a selection of techniques that assist the text analysis objective. Development of the selected techniques from their fundamental basis, through their visualization, to their demonstration in application, comprises most of the body of this research. A scientific orientation employing measurements, combined with visual depiction and explanation of the technique with limited mathematics, is used as opposed to fully utilising any one of those resulting techniques for performing complete text document analysis.

      Visualization techniques which apply directly to the text and those which exploit measurements produced by associated techniques are considered. Both approaches employ visualization to assist the human viewer to discover patterns which are then used in the analysis of the document. In the measurement case, this requires consideration of data with dimensions greater than three, which imposes a visualization difficulty. Several techniques for overcoming this problem are proposed. Word frequencies, Zipf considerations, parallel coordinates, colour maps, Cusum plots, and fractal dimensions are some of the techniques considered.

      One direct application of visualization to text documents is to assist reading of that document by de-emphasising selected words by fading them on the display from which they are read. Three word selection techniques are proposed for the automatic selection of which words to use.

      An experiment is reported which used such word fading techniques. It indicated that some readers do have improved reading speed under such conditions, but others do not. The experimental design enabled the separation of that group which did decrease reading times from the remaining readers who did not. Measurement of comprehension errors made under different types of word fading were shown not to increase beyond that obtained under normal reading conditions.

      A visualization based on categorising the words in a text document is proposed which contrasts to visualization of measurements based on counts. The result is a visual impression of the word composition, and the evolution of that composition within that document.

      The text documents used to demonstrates these techniques include English novels and short stories, emails, and a series of eighteenth century newspaper articles known as the Federalist Papers. This range of documents was needed because all analysis techniques are not applicable to all types of documents. This research proposes that an interactive use of the techniques on hand in a non-prescribed order can yield useful results in a document analysis. An example of this is in author attribution, i.e. assigning authorship of documents via patterns characteristic of an individual's writing style. Different visual techniques can be used to explore the patterns of writing in given text documents.

      A software toolkit as a platform for implementing the proposed interactive analysis of text documents is described. How the techniques could be integrated into such a toolkit is outlined. A prototype of software to implement such a toolkit is included in this research. Issues relating to implementation of each technique used are also outlined.

      Publication Type: Thesis (PhD)
      Murdoch Affiliation: School of Information Technology
      Supervisor: Turk, Andrew
      URI: http://researchrepository.murdoch.edu.au/id/eprint/177
      Item Control Page

      Downloads

      Downloads per month over past year