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LectureCapture

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dc.contributor.author Elliott, Patrick
dc.contributor.author Herzer, Brian
dc.date.accessioned 2012-05-02T17:00:02Z
dc.date.available 2012-05-02T17:00:02Z
dc.date.issued 2012-05-02
dc.identifier.uri http://hdl.handle.net/10919/18660
dc.description Lecture Capture.docx draw.js raphael-min.js style.css webpage.html en_US
dc.description.abstract The following problem was addressed by our project: How can we easily visualize the content of a body of text without manually analyzing its content? The initial goal was to be able to visualize captioned college lectures, but ended up being any section of text. Our client, Mr. James Barker of Aptigent, supplied us with a large collection of captioned news reports for us to create visualizations. These television news reports were a good examples for us, since they can usually be summarized with just a few key words and relationships between words. This obviously makes them optimal for visualizing. There were a few specifics about our task for this project. We had the ability to use a clustering program which would take a given body of text and generate, among other things, a list of keywords, which we called 'concepts,' and a list of tags. The concepts were words that the clustering program believed to have more importance, while the tags were generally words or phrases that were tied directly to one or more concepts. Our solution needed to be web-based. In order to best accomplish this task, we chose to design our solution using HTML and JavaScript. We choose to use Raphael, a JavaScript library, to draw the visualizations. Our solution puts a heavy emphasis on the proximity between each concept and tag. Whether or not the two appear in the same sentence is also taken into consideration. en_US
dc.description.sponsorship James Barker en_US
dc.language.iso en_US en_US
dc.subject Lecture Capture Cluster Analyze Visualization en_US
dc.title LectureCapture en_US
dc.type Software en_US
dc.type Technical Report en_US


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