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Show Notes
Text as Data (Justin Grimmer)
- Amazon USA Store: https://www.amazon.com/dp/0691207550?tag=9natree-20
- Amazon Worldwide Store: https://global.buys.trade/Text-as-Data-Justin-Grimmer.html
- Apple Books: https://books.apple.com/us/audiobook/data-as-a-product-a-comprehensive-guide-on-how-to/id1648211851?itsct=books_box_link&itscg=30200&ls=1&at=1001l3bAw&ct=9natree
- eBay: https://www.ebay.com/sch/i.html?_nkw=Text+as+Data+Justin+Grimmer+&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339060787&customid=9natree&toolid=10001&mkevt=1
- Read more: https://english.9natree.com/read/0691207550/
#Textrepresentation #Conceptdiscovery #Textualmeasurement #Outofsampleprediction #Textbasedcausalinference #TextasData
Text as Data: A New Framework for Machine Learning and the Social Sciences is a methodological guide by Justin Grimmer, Margaret E. Roberts, and Brandon M. Stewart. Published by Princeton University Press, it addresses a central challenge of contemporary research: how to turn abundant digital text into credible evidence about the social world. Rather than presenting machine learning as a collection of algorithms to be selected by technical convention, the book organizes analysis around the substantive purposes that textual evidence can serve. Its framework covers representation, discovery, measurement, prediction, and causal inference. Across these tasks, the authors argue that computation must be integrated with clear concepts, research design, and validation. Textual scale does not by itself resolve problems of ambiguity, bias, or inference. The book therefore treats researchers as active designers of an iterative process in which questions, data, models, and evaluation inform one another. It is aimed primarily at social scientists, but its approach also speaks to digital humanities, public policy, government, and industry analysts working with documents, messages, media, or archives.
- Amazon USA Store: https://www.amazon.com/dp/0691207550?tag=9natree-20
- Amazon Worldwide Store: https://global.buys.trade/Text-as-Data-Justin-Grimmer.html
- Apple Books: https://books.apple.com/us/audiobook/data-as-a-product-a-comprehensive-guide-on-how-to/id1648211851?itsct=books_box_link&itscg=30200&ls=1&at=1001l3bAw&ct=9natree
- eBay: https://www.ebay.com/sch/i.html?_nkw=Text+as+Data+Justin+Grimmer+&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339060787&customid=9natree&toolid=10001&mkevt=1
- Read more: https://english.9natree.com/read/0691207550/
#Textrepresentation #Conceptdiscovery #Textualmeasurement #Outofsampleprediction #Textbasedcausalinference #TextasData
Text as Data: A New Framework for Machine Learning and the Social Sciences is a methodological guide by Justin Grimmer, Margaret E. Roberts, and Brandon M. Stewart. Published by Princeton University Press, it addresses a central challenge of contemporary research: how to turn abundant digital text into credible evidence about the social world. Rather than presenting machine learning as a collection of algorithms to be selected by technical convention, the book organizes analysis around the substantive purposes that textual evidence can serve. Its framework covers representation, discovery, measurement, prediction, and causal inference. Across these tasks, the authors argue that computation must be integrated with clear concepts, research design, and validation. Textual scale does not by itself resolve problems of ambiguity, bias, or inference. The book therefore treats researchers as active designers of an iterative process in which questions, data, models, and evaluation inform one another. It is aimed primarily at social scientists, but its approach also speaks to digital humanities, public policy, government, and industry analysts working with documents, messages, media, or archives.
