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Related Experiment Video

Updated: May 7, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

UTOPIAN: user-driven topic modeling based on interactive nonnegative matrix factorization.

Jaegul Choo1, Changhyun Lee, Chandan K Reddy

  • 1Georgia Institute of Technology.

IEEE Transactions on Visualization and Computer Graphics
|September 21, 2013
PubMed
Summary
This summary is machine-generated.

We introduce UTOPIAN, a visual analytics system for topic modeling that overcomes limitations of Latent Dirichlet Allocation (LDA). UTOPIAN offers user-driven control for more consistent and reliable text analysis results.

Related Experiment Videos

Last Updated: May 7, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Area of Science:

  • Data Science
  • Information Visualization
  • Natural Language Processing

Background:

  • Topic modeling analyzes text document collections, with probabilistic graphical models like Latent Dirichlet Allocation (LDA) being prominent.
  • Existing LDA methods face challenges with consistency across runs, empirical convergence, and integrating user feedback.
  • Visual text analytics benefits from topic modeling but requires more robust and interactive solutions.

Purpose of the Study:

  • To develop a reliable and flexible visual analytics system for topic modeling.
  • To address the limitations of consistency, convergence, and user feedback integration in existing topic modeling techniques.
  • To enable users to interactively guide and steer topic modeling results.

Main Methods:

  • Propose UTOPIAN (User-driven Topic modeling based on Interactive Nonnegative Matrix Factorization), a novel visual analytics system.
  • Utilize a semi-supervised formulation enabling user interaction and control over the topic modeling process.
  • Employ Nonnegative Matrix Factorization (NMF) as the core technique for topic extraction.

Main Results:

  • UTOPIAN provides a user-driven approach to topic modeling, enhancing control and flexibility.
  • The system demonstrates improved consistency and reliability compared to traditional probabilistic methods.
  • Successful application of UTOPIAN shown through real-world document corpora, including InfoVis/VAST papers and product reviews.

Conclusions:

  • UTOPIAN offers a significant advancement in visual text analytics for topic modeling.
  • The interactive, user-driven nature of UTOPIAN allows for more tailored and accurate topic discovery.
  • This system provides a valuable tool for researchers and practitioners working with large text datasets.