Logistic Regression
This video discusses logistic regression as an entry point to machine learning for text and literary analysis. The video walks through how to train a model in a Google Drive spreadsheet for simplicity as well as how to run more complex logistical regressions in a Jupyter Notebook with Python. It also explores the results one can get when using these types of models for literary analysis.
Further Reading and Resources
- Guido, Sarah, and Andreas Müller. Introduction to Machine Learning with Python: A Guide for Data Scientists. O’Reilly Media, 2016. (Supplemental Materials at https://github.com/amueller/introduction_to_ml_with_python)
- Ignatow, Gabe, and Rada Mihalcea. “Supervised Learning,” An Introduction to Text Mining: Research Design, Data Collection, and Analysis. Thousand Oaks, CA, Sage, 2018. 115-130.
- Jarausch, Konrad H., and Kenneth A. Hardy. Quantitative Methods for Historians: A Guide to Research, Data, and Statistics. UNC Press Books, 2016.
- Lee, Susan . “Building A Logistic Regression in Python, Step by Step,” Towards Data Science. 28 September, 2017.
- “Logistic Regression,” Scikit-Learn User Guide.
Machine Learning,
Corpus Linguistics,
Computational Linguistics,
Text Mining and Analytics
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Matt Lavin is a Clinical Assistant Professor of English at the University of Pittsburgh, and Director of the department’s Digital Media Lab. His scholarship has appeared in Studies in the Novel, Literary and Linguistic Computing, Auto|Biography Studies, and The Programming Historian.
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