Abstract
This chapter will introduce concepts and techniques for using unstructured text as a data source. We will first review examples of the types of extant text data that you may encounter. We will then discuss the process of turning that text into a data source more amenable to the types of quantitative analysis that we are likely to perform.
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Notes
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Part of the reason that they can be a bad signal is due to URL shorteners obfuscating the final destination.
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If you wish to replicate this study exactly, the CSV version of the dataset is included with the book at https://dataverse.harvard.edu/dataverse/python-book.
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References
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Reese, D. (2012). Is Sen. Claire McCaskill a moderate? The Washington Post. Retrieved August 22, 2013.
Spärck Jones, K. (1972). A statistical interpretation of term specificity and its application in retrieval. Journal of Documentation, 28, 11–21.
van der Maaten, L., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9, 2579–2605.
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Cutler, J., Dickenson, M. (2020). Case Study: Natural Language Processing. In: Computational Frameworks for Political and Social Research with Python. Textbooks on Political Analysis. Springer, Cham. https://doi.org/10.1007/978-3-030-36826-5_14
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DOI: https://doi.org/10.1007/978-3-030-36826-5_14
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