Journal
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
Volume 24, Issue 1, Pages 361-370Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TVCG.2017.2744478
Keywords
Text analytics; visual analytics; word embedding; text summarization; text classification; concepts
Categories
Funding
- NIH [R01GM114267]
- National Research Foundation of Korea (NRF) grant - Korean government (MSIP) [NRF-2016R1C1B2015924]
Ask authors/readers for more resources
Central to many text analysis methods is the notion of a concept: a set of semantically related keywords characterizing a specific object, phenomenon, or theme. Advances in word embedding allow building a concept from a small set of seed terms. However, naive application of such techniques may result in false positive errors because of the polysemy of natural language. To mitigate this problem, we present a visual analytics system called ConceptVector that guides a user in building such concepts and then using them to analyze documents. Document-analysis case studies with real-world datasets demonstrate the fine-grained analysis provided by ConceptVector. To support the elaborate modeling of concepts, we introduce a bipolar concept model and support for specifying irrelevant words. We validate the interactive lexicon building interface by a user study and expert reviews. Quantitative evaluation shows that the bipolar lexicon generated with our methods is comparable to human-generated ones.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available