TopicLens: Efficient Multi-Level Visual Topic Exploration of Large-Scale Document Collections

• Published in: IEEE transactions on visualization and computer graphics Vol. 23; no. 1; pp. 151 - 160
• Main Authors: Minjeong Kim, Kyeongpil Kang, Deokgun Park, Jaegul Choo, Elmqvist, Niklas
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Analytical models; Computational modeling; Data mining; Documents; Embedding; Lenses; magic lens; Mathematical analysis; Modelling; nonnegative matrix factorization; Real-time systems; Subjects; t-distributed stochastic neighbor embedding; text analytics; topic modeling; Two dimensional displays; Two dimensional models; Visual analytics; Visualization; Workflow
• ISSN: 1077-2626; 1941-0506; 1941-0506
• DOI: 10.1109/TVCG.2016.2598445

Topic modeling, which reveals underlying topics of a document corpus, has been actively adopted in visual analytics for large-scale document collections. However, due to its significant processing time and non-interactive nature, topic modeling has so far not been tightly integrated into a visual analytics workflow. Instead, most such systems are limited to utilizing a fixed, initial set of topics. Motivated by this gap in the literature, we propose a novel interaction technique called TopicLens that allows a user to dynamically explore data through a lens interface where topic modeling and the corresponding 2D embedding are efficiently computed on the fly. To support this interaction in real time while maintaining view consistency, we propose a novel efficient topic modeling method and a semi-supervised 2D embedding algorithm. Our work is based on improving state-of-the-art methods such as nonnegative matrix factorization and t-distributed stochastic neighbor embedding. Furthermore, we have built a web-based visual analytics system integrated with TopicLens. We use this system to measure the performance and the visualization quality of our proposed methods. We provide several scenarios showcasing the capability of TopicLens using real-world datasets.