000			01875nam a22002057a 4500
999			_c4522 _d4522
005			20230208114832.0
008			230208b ||||| |||| 00| 0 eng d
020			_a9780691207551
082			_a006.312 _bGRI
100			_aGrimmer, Justin _910536
245			_aText as data: _ba new framework for machine learning and the social sciences
260			_bPrinceton University Press _aPrinceton _c2022
300			_axix, 336 p.
365			_aUSD _b39.95
520			_aFrom 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.
650			_aMachine learning _92343
650			_aSocial sciences--Data processing _910562
650			_aText data mining _911380
942			_2ddc _cBK