Bibliography – Topic Modeling

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). https://doi.org/10.46298/jdmdh.7595. Cite
Kapadia, Shashank. “Evaluate Topic Models: Latent Dirichlet Allocation (LDA).” Medium, 2020. https://towardsdatascience.com/evaluate-topic-model-in-python-latent-dirichlet-allocation-lda-7d57484bb5d0. Cite
Lee, Ashley S., Poom Chiarawongse, Jo Guldi, and Andras Zsom. “The Role of Critical Thinking in Humanities Infrastructure: The Pipeline Concept with a Study of HaToRI (Hansard Topic Relevance Identifier).” Digital Humanities Quarterly 14, no. 3 (2020). http://www.digitalhumanities.org/dhq/vol/14/3/000481/000481.html. Cite
Klein, Lauren F. “Dimensions of Scale: Invisible Labor, Editorial Work, and the Future of Quantitative Literary Studies.” PMLA 135, no. 1 (2020): 23–39. https://doi.org/10.1632/pmla.2020.135.1.23. Cite
Pardo-Guerra, Juan Pablo. “Doing Things with Bags-of-Words.” Scatterplot (blog), 2020. https://scatter.wordpress.com/2020/02/19/doing-things-with-bags-of-words/. Cite
Lee, James Jaehoon, and Joshua Beckelhimer. “Anthropocene and Empire: Discourse Networks of the Human Record.” PMLA/Publications of the Modern Language Association of America 135, no. 1 (2020): 110–29. https://doi.org/10.1632/pmla.2020.135.1.110. Cite
Allen, Colin, and Jaimie Murdock. “LDA Topic Modeling: Contexts for the History & Philosophy of Science.” Preprint, 2020. http://philsci-archive.pitt.edu/17261/. Cite
Enderle, Scott. Topic Modeling Tool, 2019. https://github.com/senderle/topic-modeling-tool. Cite
Walter, Dror, and Yotam Ophir. “News Frame Analysis: An Inductive Mixed-Method Computational Approach.” Communication Methods and Measures 13, no. 4 (2019): 248–66. https://doi.org/10.1080/19312458.2019.1639145. Cite
Hao, Shudong, and Michael J. Paul. “An Empirical Study on Crosslingual Transfer in Probabilistic Topic Models.” arXiv, 2019. https://www.semanticscholar.org/paper/An-Empirical-Study-on-Crosslingual-Transfer-in-Hao-Paul/958506be9d5789b48ab89e95b29f56701d45e46a. Cite
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
Carassai, Mauro. “Preliminary Notes on Conceptual Issues Affecting Interpretation of Topic Models.” WE1S (blog), 2018. https://we1s.ucsb.edu/research_post/preliminary-notes-on-conceptual-issues-affecting-interpretation-of-topic-models/. 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. https://doi.org/10.1109/ICSC.2018.00035. Cite
George, Clint P., and Hani Doss. “Principled Selection of Hyperparameters in the Latent Dirichlet Allocation Model.” Journal of Machine Learning Research 18, no. 162 (2018): 1–38. http://jmlr.org/papers/v18/15-595.html. Cite
Ylä-Anttila, Tuukka, Veikko Eranti, and Anna Kukkonen. “Topic Modeling as a Method for Frame Analysis: Data Mining the Climate Change Debate in India and the USA.” Preprint. SocArXiv, 2018. https://doi.org/10.31235/osf.io/dgc38. Cite
Yoo, Alex. Automatic Topic Labeling in 2018: History and Trends, 2018. https://medium.com/datadriveninvestor/automatic-topic-labeling-in-2018-history-and-trends-29c128cec17. 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
Bail, Christopher A. Topic Modeling, 2018. https://cbail.github.io/SICSS_Topic_Modeling.html. 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. https://doi.org/10.1145/3172944.3172964. Cite
Curran, Ben, Demival Vasques Filho, Kyle Higham, and Elisenda Ortiz. “Look Who’s Talking: Two-Mode Networks as Representations of a Topic Model of New Zealand Parliamentary Speeches.” PLoS ONE 13, no. 6 (2018): 1–16. https://doi.org/10.1371/journal.pone.0199072. Cite
Krstovski, Kriste, Michael J. Kurtz, David A. Smith, and Alberto Accomazzi. “Multilingual Topic Models.” ArXiv:1712.06704 [Cs, Stat], 2017. http://arxiv.org/abs/1712.06704. Cite
Dewi, Andisa, and Kilian Thiel. “Topic Extraction: Optimizing the Number of Topics with the Elbow Method.” KNIME (blog), 2017. https://www.knime.com/blog/topic-extraction-optimizing-the-number-of-topics-with-the-elbow-method. Cite
Liu, Alan, Scott Kleinman, Jeremy Douglass, Lindsay Thomas, Ashley Champagne, and Jamal Russell. “Open, Shareable, Reproducible Workflows for the Digital Humanities: The Case of the 4Humanities.Org ‘WhatEvery1Says’ Project.” In Digital Humanities 2017 Conference Abstracts. Montreal: Alliance of Digital Humanities Organizations (ADHO), 2017. Cite
Greene, Derek, and James P. Cross. “Exploring the Political Agenda of the European Parliament Using a Dynamic Topic Modeling Approach.” Political Analysis 25, no. 1 (2017): 77–94. https://doi.org/10.1017/pan.2016.7. Cite
Murdock, Jaimie, Colin Allen, and Simon DeDeo. “Exploration and Exploitation of Victorian Science in Darwin’s Reading Notebooks.” Cognition 159 (2017): 117–26. https://doi.org/10.1016/j.cognition.2016.11.012. Cite
Ellis, Peter. Cross-Validation of Topic Modelling, 2017. http://freerangestats.info/blog/2017/01/05/topic-model-cv.html. Cite
Rubin, Timothy N., Oluwasanmi Koyejo, Krzysztof J. Gorgolewski, Michael N. Jones, Russell A. Poldrack, and Tal Yarkoni. “Decoding Brain Activity Using a Large-Scale Probabilistic Functional-Anatomical Atlas of Human Cognition.” PLoS Computational Biology 13, no. 10 (2017): 1–24. https://doi.org/10.1371/journal.pcbi.1005649. Cite
Wisdom, Alyssa. Topic Modeling, 2017. https://medium.com/square-corner-blog/topic-modeling-optimizing-for-human-interpretability-48a81f6ce0ed. Cite
Erlin, Matt. “Topic Modeling, Epistemology, and the English and German Novel.” Journal of Cultural Analytics, 2017. https://doi.org/10.22148/16.014. Cite
Schofield, Alexandra, Laure Thompson, and David Mimno. “Quantifying the Effects of Text Duplication on Semantic Models.” In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2737–47. Copenhagen, Denmark: Association for Computational Linguistics, 2017. https://doi.org/10.18653/v1/D17-1290. Cite
Pashakhin, Sergey. “Topic Modeling for Frame Analysis of News Media.” In Proceeding of the Ainl Fruct 2016 Conference, 103–5. Saint-Petersburg, 2016. https://www.researchgate.net/publication/321771922_Topic_Modeling_for_Frame_Analysis_of_News_Media. Cite
Hua, Ting, Yue Ning, Feng Chen, Chang-Tien Lu, and Naren Ramakrishnan. “Topical Analysis of Interactions Between News and Social Media.” In Thirtieth AAAI Conference on Artificial Intelligence, 2016. https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12301. 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
Bhatia, Shraey, Jey Han Lau, and Timothy Baldwin. “Automatic Labelling of Topics with Neural Embeddings.” ArXiv:1612.05340 [Cs], 2016. http://arxiv.org/abs/1612.05340. Cite
Goldstone, Andrew. Dfr-Browser, 2016. https://agoldst.github.io/dfr-browser/. Cite
Gutiérrez, E. D., Ekaterina Shutova, Patricia Lichtenstein, Gerard de Melo, and Luca Gilardi. “Detecting Cross-Cultural Differences Using a Multilingual Topic Model.” Transactions of the Association for Computational Linguistics 4 (2016): 47–60. https://tacl2013.cs.columbia.edu/ojs/index.php/tacl/article/view/755. Cite
Törnberg, Anton, and Petter Törnberg. “Combining CDA and Topic Modeling: Analyzing Discursive Connections between Islamophobia and Anti-Feminism on an Online Forum , Combining CDA and Topic Modeling: Analyzing Discursive Connections between Islamophobia and Anti-Feminism on an Online Forum.” Discourse & Society 27, no. 4 (2016): 401–22. https://doi.org/10.1177/0957926516634546. Cite
Guo, Lei, Chris J. Vargo, Zixuan Pan, Weicong Ding, and Prakash Ishwar. “Big Social Data Analytics in Journalism and Mass Communication , Big Social Data Analytics in Journalism and Mass Communication: Comparing Dictionary-Based Text Analysis and Unsupervised Topic Modeling , Comparing Dictionary-Based Text Analysis and Unsupervised Topic Modeling.” Journalism & Mass Communication Quarterly 93, no. 2 (2016): 332–59. https://doi.org/10.1177/1077699016639231. Cite
Schöch, Christof. Topic Modeling with MALLET: Hyperparameter Optimization, 2016. https://dragonfly.hypotheses.org/1051. 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
Soltoff, Benjamin. Text Analysis: Topic Modeling, 2016. https://cfss.uchicago.edu/fall2016/text02.html. Cite
Hoque, Enamul, and Giuseppe Carenini. “Interactive Topic Modeling for Exploring Asynchronous Online Conversations: Design and Evaluation of ConVisIT.” ACM Transactions on Interactive Intelligent Systems 6, no. 1 (2016): 1–24. https://doi.org/10.1145/2854158. Cite
Joo, Won-Tae, Y. S. Jeong, and KyoJoong Oh. “Political Orientation Detection on Korean Newspapers via Sentence Embedding and Deep Learning.” In 2016 International Conference on Big Data and Smart Computing (BigComp), 502–4, 2016. https://doi.org/10.1109/BIGCOMP.2016.7425979. Cite
Zhao, Yanyan, Bing Qin, Ting Liu, and Duyu Tang. “Social Sentiment Sensor: A Visualization System for Topic Detection and Topic Sentiment Analysis on Microblog.” Multimedia Tools and Applications; Dordrecht 75, no. 15 (2016): 8843–60. https://doi.org/http://dx.doi.org/10.1007/s11042-014-2184-y. Cite
Sukhija, N., M. Tatineni, N. Brown, M. V. Moer, P. Rodriguez, and S. Callicott. “Topic Modeling and Visualization for Big Data in Social Sciences.” In 2016 Intl IEEE Conferences on Ubiquitous Intelligence Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), 1198–1205, 2016. https://doi.org/10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0183. Cite
Qin, Zengchang, Yonghui Cong, and Tao Wan. “Topic Modeling of Chinese Language beyond a Bag-of-Words.” Computer Speech & Language 40 (2016): 60–78. https://doi.org/10.1016/j.csl.2016.03.004. Cite
Guille, Adrien, and Edmundo-Pavel Soriano-Morales. “TOM: A Library for Topic Modeling and Browsing.” In Conférence Sur l’Extraction et La Gestion Des Connaissances. Actes de La 16ème Conférence Sur l’Extraction et La Gestion Des Connaissances. Reims, France, 2016. https://hal.archives-ouvertes.fr/hal-01442868. Cite
Allahyari, Mehdi, and Krys Kochut. “Discovering Coherent Topics with Entity Topic Models.” In 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI), 26–33. Omaha, NE, USA: IEEE, 2016. https://doi.org/10.1109/WI.2016.0015. Cite
Risam, Roopika. “South Asian Digital Humanities: An Overview.” South Asian Review 36, no. 3 (2015): 161–75. https://doi.org/10.1080/02759527.2015.11933040. Cite
Mützel, Sophie. “Facing Big Data: Making Sociology Relevant , Facing Big Data: Making Sociology Relevant.” Big Data & Society 2, no. 2 (2015): 2053951715599179. https://doi.org/10.1177/2053951715599179. Cite