Bibliography – Topic Model Interpretation

Selected DH research and resources bearing on, or utilized by, the WE1S project.
(all) Distant Reading | Cultural Analytics | Sociocultural Approaches | Topic Modeling in DH


Kessel, Patrick van. Overcoming the Limitations of Topic Models with a Semi-Supervised Approach, 2019. https://medium.com/pew-research-center-decoded/overcoming-the-limitations-of-topic-models-with-a-semi-supervised-approach-b947374e0455. Cite
Sherkat, Ehsan, Seyednaser Nourashrafeddin, Evangelos E. Milios, and Rosane Minghim. “Interactive Document Clustering Revisited: A Visual Analytics Approach.” In 23rd International Conference on Intelligent User Interfaces, 281–292. IUI ’18. New York, NY, USA: ACM, 2018. https://doi.org/10.1145/3172944.3172964. Cite
Thompson, Laure, and David Mimno. “Authorless Topic Models: Biasing Models Away from Known Structure,” 2018, 12. http://www.cs.cornell.edu/ laurejt/papers/authorless-tms-2018.pdf. Cite
Alexander, Eric, and Michael Gleicher. “Task-Driven Comparison of Topic Models.” IEEE Transactions on Visualization and Computer Graphics 22, no. 1 (2016): 320–29. https://doi.org/10.1109/TVCG.2015.2467618. Cite
Paul, Michael J. “Interpretable Machine Learning: Lessons from Topic Modeling.” In CHI Workshop on Human-Centered Machine Learning, 2016. https://cmci.colorado.edu/~mpaul/files/chi16hcml_interpretable.pdf. Cite
Boyer, Ryan. “A Human-in-the-Loop Methodology For Applying Topic Models to Identify Systems Thinking and to Perform Systems Analysis.” PhD Thesis, University of Virginia, 2016. https://doi.org/10.18130/V3NS51. Cite
Veas, Eduardo, and Cecelia di Sciascio. “Interactive Topic Analysis with Visual Analytics and Recommender Systems.” In Association for the Advancement of Artificial Intelligence. Association for the Advancement of Artificial Intelligence, 2015. http://cognitum.ws/wp-content/uploads/2015/06/VeasDiSciascio2015.pdf. Cite
Findlater, Leah, Jordan L. Boyd-Graber, Yuening Hu, Jason Chuang, and Alison Smith. Concurrent Visualization of Relationships between Words and Topics in Topic Models, 2014. /paper/Concurrent-Visualization-of-Relationships-between-Smith-Chuang/096ed34cd5d56b5daea50336f891dc26a32b981d. Cite
Boyd-Graber, Jordan, David Mimno, and David Newman. “Care and Feeding of Topic Models: Problems, Diagnostics, and Improvements.” Handbook of Mixed Membership Models and Their Applications, 2014. https://doi.org/10.1201/b17520-21. Cite
Schmidt, Benjamin M. “When You Have a MALLET, Everything Looks like a Nail.” Sapping Attention, 2012. http://sappingattention.blogspot.com/2012/11/when-you-have-mallet-everything-looks.html. Cite
Posner, Miriam. Very Basic Strategies for Interpreting Results from the Topic Modeling Tool, 2012. http://miriamposner.com/blog/very-basic-strategies-for-interpreting-results-from-the-topic-modeling-tool/. Cite
Chuang, Jason, Daniel Ramage, Christopher Manning, and Jeffrey Heer. “Interpretation and Trust: Designing Model-Driven Visualizations for Text Analysis.” In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 443–452. CHI ’12. New York, NY, USA: ACM, 2012. https://doi.org/10.1145/2207676.2207738. Cite
Chang, Jonathan, Sean Gerrish, Chong Wang, Jordan L. Boyd-graber, and David M. Blei. “Reading Tea Leaves: How Humans Interpret Topic Models.” In Advances in Neural Information Processing Systems 22, edited by Y. Bengio, D. Schuurmans, J. D. Lafferty, C. K. I. Williams, and A. Culotta, 288–296. Curran Associates, Inc., 2009. http://papers.nips.cc/paper/3700-reading-tea-leaves-how-humans-interpret-topic-models.pdf. Cite