Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

7-2016

Abstract

Word cloud is a visualization form for text that is recognized for its aesthetic, social, and analytical values. Here, we are concerned with deepening its analytical value for visual comparison of documents. To aid comparative analysis of two or more documents, users need to be able to perceive similarities and differences among documents through their word clouds. However, as we are dealing with text, approaches that treat words independently may impede accurate discernment of similarities among word clouds containing different words of related meanings. We therefore motivate the principle of displaying related words in a coherent manner, and propose to realize it through modeling the latent aspects of words. Our WORD FLOCK solution brings together latent variable analysis for embedding and aspect modeling, and calibrated layout algorithm within a synchronized word cloud generation framework. We present the quantitative and qualitative results on real-life text corpora, showcasing how the word clouds are useful in preserving the information content of documents so as to allow more accurate visual comparison of documents

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

Proceedings of the 25th International Joint Conference on Artificial Intelligence IJCAI 2016: New York, July 9-15

First Page

2536

Last Page

2543

Publisher

AAAI Press

City or Country

Palo Alto, CA

Additional URL

http://www.ijcai.org/Proceedings/16/Papers/361.pdf

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