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.

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semantically related keywords visual analytics system object phenomenon elaborate modeling of concepts polysemy of natural language irrelevant words text analysis methods conceptvector word embedding italicconceptitalic terms humangenerated realworld datasets documents bipolar concept model interactive lexicon building

Deokgun Park, Seungyeon Kim, Jurim Lee, Jaegul Choo, Niklas Elmqvist, Nicholas Diakopoulos,.ConceptVector: Text Visual Analytics via Interactive Lexicon Building Using Word Embedding. 24 (1),361-370.

Deokgun Park, et al."ConceptVector: Text Visual Analytics via Interactive Lexicon Building Using Word Embedding" 24

Deokgun Park, Seungyeon Kim, Jurim Lee, Jaegul Choo, Niklas Elmqvist, Nicholas Diakopoulos,"ConceptVector: Text Visual Analytics via Interactive Lexicon Building Using Word Embedding"