Bibliography – Text Classification

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


Kwak, Haewoon, Jisun An, and Yong-Yeol Ahn. “A Systematic Media Frame Analysis of 1.5 Million New York Times Articles from 2000 to 2017.” ArXiv:2005.01803 [Cs], 2020. http://arxiv.org/abs/2005.01803. Cite
Hvitfeldt, Emil, and Julia Silge. Supervised Machine Learning for Text Analysis in R. Emil Hvitfeldt and Julia Silge, 2020. https://smltar.com/. 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
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
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
Freitas, Alex A. “Comprehensible Classification Models: A Position Paper.” In ACM SIGKDD Explorations, 15.1:1–10. Association for Computing Machinery, 2014. https://doi.org/10.1145/2594473.2594475. Cite
Hand, David J. “Classifier Technology and the Illusion of Progress.” Statistical Science 21, no. 1 (2006): 1–14. https://doi.org/10.1214/088342306000000060. 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