Bibliography – Topic Modeling

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Digital Humanities (all) (selected resources related to WE1S methods or issues; some items also included under "Data Science and Machine Learning" and "Topic Modeling")
Cultural & Social Approaches in DH | Distant Reading | 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
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
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
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
Bail, Christopher A. Topic Modeling, 2018. https://cbail.github.io/SICSS_Topic_Modeling.html. 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
Erlin, Matt. “Topic Modeling, Epistemology, and the English and German Novel.” Journal of Cultural Analytics, 2017. https://doi.org/10.22148/16.014. 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
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): 117–126. 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
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
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
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): 8843–8860. 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
Soltoff, Benjamin. Text Analysis: Topic Modeling, 2016. https://cfss.uchicago.edu/fall2016/text02.html. Cite
Schöch, Christof. Topic Modeling with MALLET: Hyperparameter Optimization, 2016. https://dragonfly.hypotheses.org/1051. 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
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), 502–504, 2016. https://doi.org/10.1109/BIGCOMP.2016.7425979. 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
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
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): 401–422. 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): 332–359. https://doi.org/10.1177/1077699016639231. 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
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
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
Vulić, Ivan, Wim De Smet, Jie Tang, and Marie-Francine Moens. “Probabilistic Topic Modeling in Multilingual Settings: An Overview of Its Methodology and Applications.” Information Processing & Management 51, no. 1 (2015): 111–147. http://keg.cs.tsinghua.edu.cn/jietang/publications/ipm15-xLiTe-IvanVulic-overview-topic-model-multilingual.pdf. Cite
Murdock, Jaimie, and Colin Allen. “Visualization Techniques for Topic Model Checking.” In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 4284–4285. AAAI’15. Austin, Texas: AAAI Press, 2015. http://dl.acm.org/citation.cfm?id=2888116.2888368. Cite
Chuang, Jason, Margaret E. Roberts, Brandon M. Stewart, Rebecca Weiss, Dustin Tingley, Justin Grimmer, and Jeffrey Heer. “TopicCheck: Interactive Alignment for Assessing Topic Model Stability.” In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 175–184. Denver: Association for Computational Linguistics, 2015. https://doi.org/10.3115/v1/N15-1018. Cite
Buurma, Rachel Sagner. “The Fictionality of Topic Modeling: Machine Reading Anthony Trollope’s Barsetshire Series.” Big Data & Society 2, no. 2 (2015): 2053951715610591. https://doi.org/10.1177/2053951715610591. 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
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
Evans, Michael S. “A Computational Approach to Qualitative Analysis in Large Textual Datasets.” PLOS ONE 9, no. 2 (February 3, 2014): e87908. https://doi.org/10.1371/journal.pone.0087908. 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
Goldstone, Andrew, Susan Galan, C. Laura Lovin, Andrew Mazzaschi, and Lindsey Whitmore. An Interactive Topic Model of Signs. Signs at 40., 2014. http://signsat40.signsjournal.org/topic-model/#/about. Cite
Goldstone, Andrew, and Ted Underwood. “The Quiet Transformations of Literary Studies: What Thirteen Thousand Scholars Could Tell Us.” New Literary History 45, no. 3 (2014): 359–384. https://doi.org/10.1353/nlh.2014.0025. Cite
Sievert, Carson, and Kenneth R. Shirley. “LDAvis : A Method for Visualizing and Interpreting Topics.” In Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces, 63–70. Association for Computational Linguistics, 2014. http://www.aclweb.org/anthology/W14-3110. Cite
Roland, Teddy. “Topic Modeling: What Humanists Actually Do With It.” A Guest Post by Teddy Roland, University of California, Berkeley \textbar Digital Humanities, 2014. http://digitalhumanities.berkeley.edu/blog/16/07/14/topic-modeling-what-humanists-actually-do-it-guest-post-teddy-roland-university. Cite
Hoyt, Eric. “Lenses for Lantern: Data Mining, Visualization, and Excavating Film History’s Neglected Sources.” Film History 26, no. 2 (2014): 146–168. https://doi.org/10.2979/filmhistory.26.2.146. Cite
Gleicher, Michael, Michael Witmore, Robin Valenza, Joe Kohlmann, and Eric Alexander. “Serendip: Topic Model-Driven Visual Exploration of Text Corpora,” 173–182. IEEE, 2014. https://doi.org/10.1109/VAST.2014.7042493. Cite
Binder, J. M., and C. Jennings. “Visibility and Meaning in Topic Models and 18th-Century Subject Indexes.” Literary and Linguistic Computing 29, no. 3 (2014): 405–411. https://doi.org/10.1093/llc/fqu017. Cite
Tech, Digital Humanities Lab at Georgia. TOME: Interactive TOpic Model and MEtadata Visualization, 2014. https://dhlab.lmc.gatech.edu/tome/. Cite
Aletras, Nikolaos, and Mark Stevenson. “Labelling Topics Using Unsupervised Graph-Based Methods.” In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 631–636. Baltimore, Maryland: Association for Computational Linguistics, 2014. https://doi.org/10.3115/v1/P14-2103. 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
Koch, Henriette, Julie A. E. Christensen, Rune Frandsen, Marielle Zoetmulder, Lars Arvastson, Soren R. Christensen, Poul Jennum, and Helge B. D. Sorensen. “Automatic Sleep Classification Using a Data-Driven Topic Model Reveals Latent Sleep States.” Journal of Neuroscience Methods 235 (2014): 130–137. https://doi.org/10.1016/j.jneumeth.2014.07.002. Cite
Goldstone, Andrew, Susana Galán, C. Laura Lovin, Andrew Mazzaschi, and Lindsay Whitmore, eds. “An Interactive Topic Model of Signs (Visualized in Dfr-Browser).” Signs, 2014. http://signsat40.signsjournal.org/topic-model/. Cite