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

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    ConceptVector aids users in building and analyzing text concepts using word embeddings. This visual analytics system mitigates polysemy issues for more accurate document analysis.

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    Area of Science:

    • Natural Language Processing
    • Information Visualization
    • Human-Computer Interaction

    Background:

    • Text analysis relies on identifying concepts, often built from keywords.
    • Word embeddings enable concept creation from seed terms but face polysemy challenges.
    • Existing methods may produce false positives due to word ambiguity.

    Purpose of the Study:

    • Introduce ConceptVector, a visual analytics system for concept-based text analysis.
    • Address limitations of naive word embedding applications in concept modeling.
    • Enhance document analysis through guided concept building and refinement.

    Main Methods:

    • Developed a visual analytics system (ConceptVector) for interactive concept construction.
    • Introduced a bipolar concept model and irrelevant word specification.
    • Utilized word embeddings for semantic keyword expansion.
    • Validated the system through document-analysis case studies and user studies.

    Main Results:

    • ConceptVector enables fine-grained document analysis.
    • The interactive lexicon building interface was validated by user studies and expert reviews.
    • Generated bipolar lexicons are quantitatively comparable to human-generated ones.

    Conclusions:

    • ConceptVector effectively guides users in building robust text concepts.
    • The system mitigates polysemy issues, improving text analysis accuracy.
    • Visual analytics combined with advanced modeling offers a powerful approach to concept-based text mining.