Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework

Contributors

Mennatallah El-Assady, Rita Sevastjanova, Fabian Sperrle, Daniel Keim, and Christopher Collins

Abstract

Topic modeling algorithms are widely used to analyze the thematic composition of text corpora but remain difficult to interpret and adjust. Addressing these limitations, we present a modular visual analytics framework, tackling the understandability and adaptability of topic models through a user-driven reinforcement learning process which does not require a deep understanding of the underlying topic modeling algorithms. Given a document corpus, our approach initializes two algorithm configurations based on a parameter space analysis that enhances document separability. We abstract the model complexity in an interactive visual workspace for exploring the automatic matching results of two models, investigating topic summaries, analyzing parameter distributions, and reviewing documents. The main contribution of our work is an iterative decision-making technique in which users provide a document-based relevance feedback that allows the framework to converge to a user-endorsed topic distribution. We also report feedback from a two-stage study which shows that our technique results in topic model quality improvements on two independent measures.

Demo

To apply our technique on your own data or try out a demo, please visit: http://visargue.dbvis.de/ (Individual accounts will be created upon request).

Publications

  • M. El-Assady, R. Sevastjanova, F. Sperrle, D. Keim, and C. Collins, “Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework,” IEEE Transactions on Visualization and Computer Graphics, vol. PP, 2017. Honorable Mention for Best Paper.
    [Bibtex] [PDF] [DOI]
    @Article{ElA2017b,
      author =    {Mennatallah El-Assady and Rita Sevastjanova and Fabian Sperrle and Daniel Keim and Christopher Collins},
      title =    {Progressive Learning of Topic Modeling Parameters: A Visual Analytics Framework},
      journal = {IEEE Transactions on Visualization and Computer Graphics},
      doi = {10.1109/TVCG.2017.2745080},
      volume = PP,
      year =   2017,
      note = {Honorable Mention for Best Paper}
    }

VAST Award - Honorable Mention for Best Paper

Videos

Demo Video:

Talk from IEEE VAST 2017:

Research

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