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    This study introduces Human-steerable Topic Modeling (HSTM) to improve topic extraction. The new method enhances Latent Dirichlet Allocation (LDA) with user control and visual guidance for better results.

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    Area of Science:

    • Natural Language Processing
    • Machine Learning
    • Information Retrieval

    Background:

    • Traditional topic models often rely on matrix decomposition, which can be complex.
    • Latent Dirichlet Allocation (LDA) is popular but has limitations in control and search space.
    • Existing methods lack effective user guidance for refining topic extraction.

    Purpose of the Study:

    • To present a novel Human-steerable Topic Modeling (HSTM) technique.
    • To address the complexity and stochastic nature of LDA.
    • To improve the efficiency of exploring large corpora for relevant topics.

    Main Methods:

    • Extended Latent Dirichlet Allocation (LDA) as the core topic extraction component.
    • Developed a weighting method to incorporate user refinements into LDA's Gibbs sampling.
    • Designed a visual editing framework utilizing the coherence metric for interactive refinement.

    Main Results:

    • Demonstrated usability and effectiveness on two real-world datasets.
    • User study participants showed positive performance with the HSTM technique.
    • Quantitative experiments confirmed the technique's ability to guide refinements.

    Conclusions:

    • The proposed HSTM technique offers a controllable and effective approach to topic modeling.
    • Integrating user feedback and visual guidance enhances LDA's applicability.
    • HSTM provides a more intuitive and efficient way to explore and refine topics in large datasets.