ConceptVector: Text Visual Analytics via Interactive Lexicon Building using Word Embedding

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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.
Publisher
IEEE COMPUTER SOC
Issue Date
2018-01
Language
English
Article Type
Article; Proceedings Paper
Citation

IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, v.24, no.1, pp.361 - 370

ISSN
1077-2626
DOI
10.1109/TVCG.2017.2744478
URI
http://hdl.handle.net/10203/273419
Appears in Collection
RIMS Journal Papers
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