國立虎尾科技大學 |

Visual Analytics for Open-ended Tasks in Text Mining.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Visual Analytics for Open-ended Tasks in Text Mining./
Author:	Park, Deokgun.
Description:	1 online resource (193 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Contained By:	Dissertation Abstracts International79-12B(E).
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9780438158566

Visual Analytics for Open-ended Tasks in Text Mining.
Park, Deokgun.

Visual Analytics for Open-ended Tasks in Text Mining. - 1 online resource (193 pages)

Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.

Thesis (Ph.D.)--University of Maryland, College Park, 2018.

Includes bibliographical references

Overview 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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780438158566Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Visual Analytics for Open-ended Tasks in Text Mining.
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300 $a 1 online resource (193 pages)
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500 $a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
500 $a Adviser: Niklas Elmqvist.
502 $a Thesis (Ph.D.)--University of Maryland, College Park, 2018.
504 $a Includes bibliographical references
520 $a Overview 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.
520 $a 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.
520 $a 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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Maryland, College Park. $b Computer Science. $3 1180862
773 0 $t Dissertation Abstracts International $g 79-12B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10792232 $z click for full text (PQDT)