Sinossi
Learn how to conduct a robust text analysis project from start to finish--and then do it again.
Mining is the dominant metaphor in computational text analysis. When mining texts, the implied assumption is that analysts can find kernels of truth--they just have to sift through the rubbish first. In this book, Dustin Stoltz and Marshall Taylor encourage text analysts to work with a different metaphor in mind: mapping. When mapping texts, the goal is not necessarily to find meaningful needles in the haystack, but instead to create reductions of the text to document patterns. Just like with cartographic maps, though, the type and nature of the textual map is dependent on a range of decisions on the part of the researcher. Creating reproducible workflows is therefore critical for the text analyst.
Mapping Texts offers a practical introduction to computational text analysis with step-by-step guides on how to conduct actual text analysis workflows in the R statistical computing environment. The focus is on social science questions and applications, with data ranging from fake news and presidential campaigns to Star Trek and pop stars. The book walks the reader through all facets of a text analysis workflow--from understanding the theories of language embedded in text analysis, all the way to more advanced and cutting-edge techniques.
The book will prove useful not only to social scientists, but anyone interested in conducting text analysis projects.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Informazioni sull?autore
Dustin S. Stoltz is an assistant professor of sociology and cognitive science at Lehigh University. His research explores how social structure, culture, and cognition shapes ideas and evaluations.
Marshall A. Taylor is an assistant professor of sociology at New Mexico State University. His research focuses on questions of cognition and measurement in the sociology of culture.
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Hardcover. Condizione: new. Hardcover. Learn how to conduct a robust text analysis project from start to finish--and then do it again. Mining is the dominant metaphor in computational text analysis. When mining texts, the implied assumption is that analysts can find kernels of truth--they just have to sift through the rubbish first. In this book, Dustin Stoltz and Marshall Taylor encourage text analysts to work with a different metaphor in mind: mapping. Whenmapping texts, the goal is not necessarily to find meaningful needles in the haystack, but instead to create reductions of the text to document patterns. Just like with cartographic maps, though, the type and nature of thetextual map is dependent on a range of decisions on the part of the researcher. Creating reproducible workflows is therefore critical for the text analyst.Mapping Texts offers a practical introduction to computational text analysis with step-by-step guides on how to conduct actual text analysis workflows in the R statistical computing environment. The focus is on social science questions and applications, with data ranging from fake news and presidential campaignsto Star Trek and pop stars. The book walks the reader through all facets of a text analysis workflow--from understanding the theories of language embedded in text analysis, all the way to more advanced andcutting-edge techniques.The book will prove useful not only to social scientists, but anyone interested in conducting text analysis projects. Mapping Texts is the first introduction to computational text analysis that simultaneously blends conceptual treatments with practical, hands-on examples that walk the reader through how to conduct text analysis projects with real data. The book shows how to conduct text analysis in the R statistical computing environment--a popular programming language in data science. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9780197756874
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Hardback. Condizione: New. Learn how to conduct a robust text analysis project from start to finish--and then do it again. Mining is the dominant metaphor in computational text analysis. When mining texts, the implied assumption is that analysts can find kernels of truth--they just have to sift through the rubbish first. In this book, Dustin Stoltz and Marshall Taylor encourage text analysts to work with a different metaphor in mind: mapping. When mapping texts, the goal is not necessarily to find meaningful needles in the haystack, but instead to create reductions of the text to document patterns. Just like with cartographic maps, though, the type and nature of the textual map is dependent on a range of decisions on the part of the researcher. Creating reproducible workflows is therefore critical for the text analyst.Mapping Texts offers a practical introduction to computational text analysis with step-by-step guides on how to conduct actual text analysis workflows in the R statistical computing environment. The focus is on social science questions and applications, with data ranging from fake news and presidential campaigns to Star Trek and pop stars. The book walks the reader through all facets of a text analysis workflow--from understanding the theories of language embedded in text analysis, all the way to more advanced and cutting-edge techniques.The book will prove useful not only to social scientists, but anyone interested in conducting text analysis projects. Codice articolo LU-9780197756874
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Hardcover. Condizione: new. Hardcover. Learn how to conduct a robust text analysis project from start to finish--and then do it again. Mining is the dominant metaphor in computational text analysis. When mining texts, the implied assumption is that analysts can find kernels of truth--they just have to sift through the rubbish first. In this book, Dustin Stoltz and Marshall Taylor encourage text analysts to work with a different metaphor in mind: mapping. Whenmapping texts, the goal is not necessarily to find meaningful needles in the haystack, but instead to create reductions of the text to document patterns. Just like with cartographic maps, though, the type and nature of thetextual map is dependent on a range of decisions on the part of the researcher. Creating reproducible workflows is therefore critical for the text analyst.Mapping Texts offers a practical introduction to computational text analysis with step-by-step guides on how to conduct actual text analysis workflows in the R statistical computing environment. The focus is on social science questions and applications, with data ranging from fake news and presidential campaignsto Star Trek and pop stars. The book walks the reader through all facets of a text analysis workflow--from understanding the theories of language embedded in text analysis, all the way to more advanced andcutting-edge techniques.The book will prove useful not only to social scientists, but anyone interested in conducting text analysis projects. Mapping Texts is the first introduction to computational text analysis that simultaneously blends conceptual treatments with practical, hands-on examples that walk the reader through how to conduct text analysis projects with real data. The book shows how to conduct text analysis in the R statistical computing environment--a popular programming language in data science. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Codice articolo 9780197756874
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