UTOPIAN: User-Driven Topic Modeling Based on Interactive Nonnegative Matrix Factorization

By J. Choo, Changhyun Lee, Chandan K. Reddy, Haesun Park. IEEE Transactions on Visualization and Computer Graphics, 2013

306 citations recorded by the source corpus.

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Applications of Topic Models (2017, background): “In contrast to probabilistic constraints, Choo et al. [2013] and Lund et al. [2017] use matrix factorization constraints to guide changes to topics, which can be much faster.”
Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models (2018, background): “Typical solutions include improving hyperparameters [9, 13], features [6,26,43], or training instances [29] and investigating multiple models in order to acquire an optimal model ensemble [35, 38, 43].”
Probabilistic Latent Document Network Embedding (2014, methodology): “Our work builds on the Relational Topic Model (RTM) [23], which we review briefly below.”
Informed Machine Learning – A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems (2019, methodology): “Lastly, various tools exist for text analysis, in particular for topic modeling [97] where users can create, merge and refine topics or change keyword weights.”
Towards better analysis of machine learning models: A visual analytics perspective (2017, methodology): “From the information visualization community, researchers have designed a number of techniques for user initiative guidance [14, 30, 33, 34, 49], where the user is the active participant in improving and refining the performance and learning results.”