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    Clustered Layout Word Cloud for User Generated Online Reviews

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    Date
    2012-11-20
    Author
    Wang, Ji
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    Abstract
    User generated reviews, like those found on Yelp and Amazon, have become important refer- ence material in casual decision making, like dining, shopping and entertainment. However, very large amounts of reviews make the review reading process time consuming. A text visualization can speed up the review reading process.

    In this thesis, we present the clustered layout word cloud -- a text visualization that quickens decision making based on user generated reviews. We used a natural language processing approach, called grammatical dependency parsing, to analyze user generated review content and create a semantic graph. A force-directed graph layout was applied to the graph to create the clustered layout word cloud.

    We conducted a two-task user study to compare the clustered layout word cloud to two alternative review reading techniques: random layout word cloud and normal block-text reviews. The results showed that the clustered layout word cloud offers faster task completion time and better user satisfaction than the other two alternative review reading techniques. [Permission email from J. Huang removed at his request. GMc March 11, 2014]
    URI
    http://hdl.handle.net/10919/19193
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    • Masters Theses [19662]

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