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Text as Data

- A New Framework for Machine Learning and the Social Sciences
Af: Brandon M. Stewart, Justin Grimmer, Margaret E. Roberts Engelsk Hardback

Text as Data

- A New Framework for Machine Learning and the Social Sciences
Af: Brandon M. Stewart, Justin Grimmer, Margaret E. Roberts Engelsk Hardback
Tjek vores konkurrenters priser
A guide for using computational text analysis to learn about the social world From social media posts and text messages to digital government documents and archives, researchers are bombarded with a deluge of text reflecting the social world. This textual data gives unprecedented insights into fundamental questions in the social sciences, humanities, and industry. Meanwhile new machine learning tools are rapidly transforming the way science and business are conducted. Text as Data shows how to combine new sources of data, machine learning tools, and social science research design to develop and evaluate new insights. Text as Data is organized around the core tasks in research projects using text—representation, discovery, measurement, prediction, and causal inference. The authors offer a sequential, iterative, and inductive approach to research design. Each research task is presented complete with real-world applications, example methods, and a distinct style of task-focused research. Bridging many divides—computer science and social science, the qualitative and the quantitative, and industry and academia—Text as Data is an ideal resource for anyone wanting to analyze large collections of text in an era when data is abundant and computation is cheap, but the enduring challenges of social science remain. Overview of how to use text as dataResearch design for a world of data delugeExamples from across the social sciences and industry
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A guide for using computational text analysis to learn about the social world From social media posts and text messages to digital government documents and archives, researchers are bombarded with a deluge of text reflecting the social world. This textual data gives unprecedented insights into fundamental questions in the social sciences, humanities, and industry. Meanwhile new machine learning tools are rapidly transforming the way science and business are conducted. Text as Data shows how to combine new sources of data, machine learning tools, and social science research design to develop and evaluate new insights. Text as Data is organized around the core tasks in research projects using text—representation, discovery, measurement, prediction, and causal inference. The authors offer a sequential, iterative, and inductive approach to research design. Each research task is presented complete with real-world applications, example methods, and a distinct style of task-focused research. Bridging many divides—computer science and social science, the qualitative and the quantitative, and industry and academia—Text as Data is an ideal resource for anyone wanting to analyze large collections of text in an era when data is abundant and computation is cheap, but the enduring challenges of social science remain. Overview of how to use text as dataResearch design for a world of data delugeExamples from across the social sciences and industry
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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 360
ISBN-13: 9780691207544
Indbinding: Hardback
Udgave:
ISBN-10: 0691207542
Kategori: Informationsarkitektur
Udg. Dato: 29 mar 2022
Længde: 28mm
Bredde: 262mm
Højde: 183mm
Forlag: Princeton University Press
Oplagsdato: 29 mar 2022
Forfatter(e): Brandon M. Stewart, Justin Grimmer, Margaret E. Roberts
Forfatter(e) Brandon M. Stewart, Justin Grimmer, Margaret E. Roberts

Kategori Informationsarkitektur

ISBN-13 9780691207544

Sprog Engelsk

Indbinding Hardback

Sider 360

Udgave

Længde 28mm

Bredde 262mm

Højde 183mm

Udg. Dato 29 mar 2022

Oplagsdato 29 mar 2022

Forlag Princeton University Press

Kategori sammenhænge