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

Deokgun Park, Seungyeon Kim, Jurim Lee, Jaegul Choo, Nicholas Diakopoulos, Niklas Elmqvist

Research output: Contribution to journalArticlepeer-review

63 Citations (Scopus)


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.

Original languageEnglish
Article number8023823
Pages (from-to)361-370
Number of pages10
JournalIEEE Transactions on Visualization and Computer Graphics
Issue number1
Publication statusPublished - 2018 Jan

Bibliographical note

Publisher Copyright:
© 1995-2012 IEEE.


  • Text analytics
  • concepts
  • text classification
  • text summarization
  • visual analytics
  • word embedding

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design


Dive into the research topics of 'ConceptVector: Text Visual Analytics via Interactive Lexicon Building Using Word Embedding'. Together they form a unique fingerprint.

Cite this