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ISBN 10: 0691207550 ISBN 13: 9780691207551
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ISBN 10: 0691207550 ISBN 13: 9780691207551
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Publicado por Princeton University Press, 2022
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Publicado por Princeton University Press, 2022
ISBN 10: 0691207550 ISBN 13: 9780691207551
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Idioma: Inglés
Publicado por Princeton University Press, 2022
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Publicado por Princeton University Press, 2022
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Idioma: Inglés
Publicado por Princeton University Press, 2022
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Publicado por Princeton University Press, 2022
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Publicado por Princeton University Press, 2022
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Idioma: Inglés
Publicado por Princeton University Press, 2022
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Publicado por Princeton University Press, 2022
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Idioma: Inglés
Publicado por Princeton University Press, 2022
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Idioma: Inglés
Publicado por Princeton University Press, US, 2022
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Añadir al carritoPaperback. Condición: New. 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.
Idioma: Inglés
Publicado por Princeton University Press, US, 2022
ISBN 10: 0691207550 ISBN 13: 9780691207551
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Añadir al carritoPaperback. Condición: New. 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.
Idioma: Inglés
Publicado por Princeton University Press, 2022
ISBN 10: 0691207550 ISBN 13: 9780691207551
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Idioma: Inglés
Publicado por Princeton University Press, 2022
ISBN 10: 0691207550 ISBN 13: 9780691207551
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Idioma: Inglés
Publicado por Princeton University Press, 2022
ISBN 10: 0691207550 ISBN 13: 9780691207551
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Idioma: Inglés
Publicado por Princeton University Press, 2022
ISBN 10: 0691207550 ISBN 13: 9780691207551
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Idioma: Inglés
Publicado por Princeton University Press, 2022
ISBN 10: 0691207550 ISBN 13: 9780691207551
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Idioma: Inglés
Publicado por Princeton University Press, 2022
ISBN 10: 0691207550 ISBN 13: 9780691207551
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Idioma: Inglés
Publicado por Princeton University Press 3/29/2022, 2022
ISBN 10: 0691207550 ISBN 13: 9780691207551
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Añadir al carritoPaperback or Softback. Condición: New. Text as Data: A New Framework for Machine Learning and the Social Sciences. Book.
Idioma: Inglés
Publicado por Princeton University Press 2021-11-30, 2021
ISBN 10: 0691207550 ISBN 13: 9780691207551
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Añadir al carritoPaperback. Condición: New.
Idioma: Inglés
Publicado por Princeton University Press, 2022
ISBN 10: 0691207550 ISBN 13: 9780691207551
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Idioma: Inglés
Publicado por Princeton University Press, 2022
ISBN 10: 0691207550 ISBN 13: 9780691207551
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Añadir al carritoCondición: New. Über den AutorJustin Grimmer is professor of political science and a senior fellow at the Hoover Institution at Stanford University. Twitter @justingrimmer Margaret E. Roberts is associate professor in political scienc.
Idioma: Inglés
Publicado por Princeton University Press, US, 2022
ISBN 10: 0691207550 ISBN 13: 9780691207551
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Añadir al carritoPaperback. Condición: New. 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.
Idioma: Inglés
Publicado por Princeton University Press Mär 2022, 2022
ISBN 10: 0691207550 ISBN 13: 9780691207551
Librería: AHA-BUCH GmbH, Einbeck, Alemania
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Añadir al carritoTaschenbuch. Condición: Neu. Neuware - 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 data- Research design for a world of data deluge- Examples from across the social sciences and industry.
Idioma: Inglés
Publicado por Princeton University Press, US, 2022
ISBN 10: 0691207550 ISBN 13: 9780691207551
Librería: Rarewaves.com UK, London, Reino Unido
EUR 51,28
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Añadir al carritoPaperback. Condición: New. 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.