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 | Non-consumptive Use

Shadrova, Anna. “Topic Models Do Not Model Topics: Epistemological Remarks and Steps towards Best Practices.” Journal of Data Mining & Digital Humanities 2021 (2021). Cite
Kapadia, Shashank. “Evaluate Topic Models: Latent Dirichlet Allocation (LDA).” Medium, 2020. Cite
Allen, Colin, and Jaimie Murdock. “LDA Topic Modeling: Contexts for the History & Philosophy of Science.” Preprint, 2020. Cite
Kessel, Patrick van. Overcoming the Limitations of Topic Models with a Semi-Supervised Approach, 2019. Cite
Carassai, Mauro. “Preliminary Notes on Conceptual Issues Affecting Interpretation of Topic Models.” WE1S (blog), 2018. Cite
Syed, S., and M. Spruit. “Selecting Priors for Latent Dirichlet Allocation.” In 2018 IEEE 12th International Conference on Semantic Computing (ICSC), 194–202, 2018. Cite
Thompson, Laure, and David Mimno. “Authorless Topic Models: Biasing Models Away from Known Structure,” 2018, 12. laurejt/papers/authorless-tms-2018.pdf. 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, 281–92. IUI ’18. New York, NY, USA: ACM, 2018. 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. Cite
Paul, Michael J. “Interpretable Machine Learning: Lessons from Topic Modeling.” In CHI Workshop on Human-Centered Machine Learning, 2016. 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. 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. 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. 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
Schmidt, Benjamin M. “When You Have a MALLET, Everything Looks like a Nail.” Sapping Attention (blog), 2012. Cite
Posner, Miriam. Very Basic Strategies for Interpreting Results from the Topic Modeling Tool, 2012. 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, 443–52. CHI ’12. New York, NY, USA: ACM, 2012. 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, 288–96. Curran Associates, Inc., 2009. Cite