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dc.contributor.advisorElmqvist, Niklasen_US
dc.contributor.authorPark, Deokgunen_US
dc.date.accessioned2018-07-17T06:20:02Z
dc.date.available2018-07-17T06:20:02Z
dc.date.issued2018en_US
dc.identifierhttps://doi.org/10.13016/M26T0H03F
dc.identifier.urihttp://hdl.handle.net/1903/21003
dc.description.abstractOverview of documents using topic modeling and multidimensional scaling is helpful in understanding topic distribution. While we can spot clusters visually, it is challenging to characterize them. My research investigates an interactive method to identify clusters by assigning attributes and examining the resulting distributions. ParallelSpaces examines the understanding of topic modeling applied to Yelp business reviews, where businesses and their reviews each constitute a separate visual space. Exploring these spaces enables the characterization of each space using the other. However, the scatterplot-based approach in ParallelSpaces does not generalize to categorical variables due to overplotting. My research proposes an improved layout algorithm for those cases in our follow-up work, Gatherplots, which eliminate overplotting in scatterplots while maintaining individual objects. Another limitation in clustering methods is the fixed number of clusters as a hyperparameter. TopicLens is a Magic Lens-type interaction technique, where the documents under the lens are clustered according to topics in real time. While ParallelSpaces help characterize the clusters, the attributes are sometimes limited. To extend the analysis by creating a custom mixture of attributes, CommentIQ is a comment moderation tool where moderators can adjust model parameters according to the context or goals. To help users analyze documents semantically, we develop a technique for user-driven text mining by building a dictionary for topics or concepts in a follow-up study, ConceptVector, which uses word embedding to generate dictionaries interactively and uses those dictionaries to analyze the documents. My dissertation contributes interactive methods to overview documents to integrate the user in text mining loops that currently are non-interactive. The case studies we present in this dissertation provide concrete and operational techniques for directly improving several state-of-the-art text mining algorithms. We summarize those generalizable lessons and discuss the limitations of the visual analytics approach.en_US
dc.language.isoenen_US
dc.titleVISUAL ANALYTICS FOR OPEN-ENDED TASKS IN TEXT MININGen_US
dc.typeDissertationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentComputer Scienceen_US
dc.subject.pqcontrolledComputer scienceen_US
dc.subject.pquncontrolledinformation visualizationen_US
dc.subject.pquncontrolledopen-ended tasksen_US
dc.subject.pquncontrolledtext miningen_US
dc.subject.pquncontrolledvisual analyticsen_US


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