UTOPIAN : user-driven topic modeling based on interactive nonnegative matrix factorization

Topic modeling has been widely used for analyzing text document collections. Recently, there have been significant advancements in various topic modeling techniques, particularly in the form of probabilistic graphical modeling. State-of-the-art techniques such as Latent Dirichlet Allocation (LDA) ha...

Ausführliche Beschreibung

Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on visualization and computer graphics. - 1996. - 19(2013), 12 vom: 13. Dez., Seite 1992-2001
1. Verfasser: Choo, Jaegul (VerfasserIn)
Weitere Verfasser: Lee, Changhyun, Reddy, Chandan K, Park, Haesun
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2013
Zugriff auf das übergeordnete Werk:IEEE transactions on visualization and computer graphics
Schlagworte:Journal Article Research Support, U.S. Gov't, Non-P.H.S.
LEADER 01000naa a22002652 4500
001 NLM23102018X
003 DE-627
005 20231224085747.0
007 cr uuu---uuuuu
008 231224s2013 xx |||||o 00| ||eng c
024 7 |a 10.1109/TVCG.2013.212  |2 doi 
028 5 2 |a pubmed24n0770.xml 
035 |a (DE-627)NLM23102018X 
035 |a (NLM)24051765 
040 |a DE-627  |b ger  |c DE-627  |e rakwb 
041 |a eng 
100 1 |a Choo, Jaegul  |e verfasserin  |4 aut 
245 1 0 |a UTOPIAN  |b user-driven topic modeling based on interactive nonnegative matrix factorization 
264 1 |c 2013 
336 |a Text  |b txt  |2 rdacontent 
337 |a ƒaComputermedien  |b c  |2 rdamedia 
338 |a ƒa Online-Ressource  |b cr  |2 rdacarrier 
500 |a Date Completed 02.05.2014 
500 |a Date Revised 20.09.2013 
500 |a published: Print 
500 |a Citation Status MEDLINE 
520 |a Topic modeling has been widely used for analyzing text document collections. Recently, there have been significant advancements in various topic modeling techniques, particularly in the form of probabilistic graphical modeling. State-of-the-art techniques such as Latent Dirichlet Allocation (LDA) have been successfully applied in visual text analytics. However, most of the widely-used methods based on probabilistic modeling have drawbacks in terms of consistency from multiple runs and empirical convergence. Furthermore, due to the complicatedness in the formulation and the algorithm, LDA cannot easily incorporate various types of user feedback. To tackle this problem, we propose a reliable and flexible visual analytics system for topic modeling called UTOPIAN (User-driven Topic modeling based on Interactive Nonnegative Matrix Factorization). Centered around its semi-supervised formulation, UTOPIAN enables users to interact with the topic modeling method and steer the result in a user-driven manner. We demonstrate the capability of UTOPIAN via several usage scenarios with real-world document corpuses such as InfoVis/VAST paper data set and product review data sets 
650 4 |a Journal Article 
650 4 |a Research Support, U.S. Gov't, Non-P.H.S. 
700 1 |a Lee, Changhyun  |e verfasserin  |4 aut 
700 1 |a Reddy, Chandan K  |e verfasserin  |4 aut 
700 1 |a Park, Haesun  |e verfasserin  |4 aut 
773 0 8 |i Enthalten in  |t IEEE transactions on visualization and computer graphics  |d 1996  |g 19(2013), 12 vom: 13. Dez., Seite 1992-2001  |w (DE-627)NLM098269445  |x 1941-0506  |7 nnns 
773 1 8 |g volume:19  |g year:2013  |g number:12  |g day:13  |g month:12  |g pages:1992-2001 
856 4 0 |u http://dx.doi.org/10.1109/TVCG.2013.212  |3 Volltext 
912 |a GBV_USEFLAG_A 
912 |a SYSFLAG_A 
912 |a GBV_NLM 
912 |a GBV_ILN_350 
951 |a AR 
952 |d 19  |j 2013  |e 12  |b 13  |c 12  |h 1992-2001