Bibliography – Text Analysis

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


Hvitfeldt, Emil, and Julia Silge. Supervised Machine Learning for Text Analysis in R. Emil Hvitfeldt and Julia Silge, 2020. https://smltar.com/. Cite
Rogers, Anna, Olga Kovaleva, and Anna Rumshisky. “A Primer in BERTology: What We Know about How BERT Works.” ArXiv:2002.12327 [Cs], 2020. http://arxiv.org/abs/2002.12327. Cite
Yang, Yiwei, Eser Kandogan, Yunyao Li, Prithviraj Sen, and Walter S. Lasecki. “A Study on Interaction in Human-in-the-Loop Machine Learning for Text Analytics.” In IUI Workshops 2019. Los Angeles: ACM, 2019. https://www.semanticscholar.org/paper/A-Study-on-Interaction-in-Human-in-the-Loop-Machine-Yang-Kandogan/03a4544caed21760df30f0e4f417bbe361c29c9e. Cite
“The Programming Historian.” Programming Historian, 2019. https://programminghistorian.org/. Cite
Ford, Clay. “The Wilcoxon Rank Sum Test.” University of Virginia Library Research Data Services + Sciences, 2017. https://data.library.virginia.edu/the-wilcoxon-rank-sum-test/. Cite
Steinskog, Asbjørn, Jonas Therkelsen, and Björn Gambäck. “Twitter Topic Modeling by Tweet Aggregation.” In Proceedings of the 21st Nordic Conference of Computational Linguistics, 77–86. Gothenburg: Linko¨ping University Electronic Press, 2017. https://www.semanticscholar.org/paper/Twitter-Topic-Modeling-by-Tweet-Aggregation-Steinskog-Therkelsen/89735b06ee5d7bcb469ddc619022bbc9f2443f02. Cite
Lijffijt, Jefrey, Terttu Nevalainen, Tanja Säily, Panagiotis Papapetrou, Kai Puolamäki, and Heikki Mannila. “Significance Testing of Word Frequencies in Corpora.” Literary and Linguistic Computing 31, no. 2 (2016): 374–97. https://doi.org/10.1093/llc/fqu064. Cite
Algee-Hewitt, Mark, Sarah Allison, Marissa Gemma, Ryan Heuser, Franco Moretti, and Hannah Walser. Canon/Archive: Large-Scale Dynamics in the Literary Field. Vol. 11. Stanford Literary Lab Pamphlets. Stanford, CA: Stanford Literary Lab, 2016. https://litlab.stanford.edu/LiteraryLabPamphlet11.pdf. Cite
Long, Hoyt, and Richard Jean So. “Literary Pattern Recognition: Modernism between Close Reading and Machine Learning.” Critical Inquiry 42, no. 2 (2016): 235–67. https://doi.org/10.1086/684353. Cite
Algee-Hewitt, Mark, and Mark McGurl. Between Canon and Corpus: Six Perspectives on 20th-Century Novels. Vol. 8. Stanford Literary Lab Pamphlets. Stanford, CA: Stanford Literary Lab, 2015. https://litlab.stanford.edu/LiteraryLabPamphlet8.pdf. Cite
tedunderwood. “Seven Ways Humanists Are Using Computers to Understand Text.” The Stone and the Shell, 2015. https://tedunderwood.com/2015/06/04/seven-ways-humanists-are-using-computers-to-understand-text/. Cite
De Bolla, Peter. The Architecture of Concepts: The Historical Formation of Human Rights. New York: Fordham University Press, 2013. Cite
Baker, Paul, Costas Gabrielatos, Majid KhosraviNik, Michał Krzyżanowski, Tony McEnery, and Ruth Wodak. “A Useful Methodological Synergy? Combining Critical Discourse Analysis and Corpus Linguistics to Examine Discourses of Refugees and Asylum Seekers in the UK Press.” Discourse & Society 19, no. 3 (2008): 273–306. https://doi.org/10.1177/0957926508088962. Cite
Sebastiani, Fabrizio. “Machine Learning in Automated Text Categorization.” ACM Computing Surveys (CSUR) 34, no. 1 (2002): 1–47. https://doi.org/10.1145/505282.505283. Cite