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Mapping Texts

Computational Text Analysis for the Social Sciences

Bibliographic Data

ID19977586
AuthorsDustin S Stoltz (0000-0002-4774-0765, Assistant Professor of Sociology and Cognitive Science, Lehigh University), Taylor Taylor, Melissa A Taylor (0000-0002-7440-0723, New Mexico State University), Marshall A Taylor (Assistant Professor of Sociology, New Mexico State University)
Year2023
Publication date2023-12-14
Open AccessNo
TypeBOOK
VenueMapping Texts (SOURCE_BOOK)
PublisherOxford University PressNew York (PUBLISHER)
DOI10.1093/oso/9780197756874.001.0001
OpenAlexW4389732436
Open LibraryOL49612826M
ISBN9780197756874
LanguageEN
Citations received10
References cited114

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 rubbish first. In this book, Stoltz and Taylor encourage text analysts to work with a different metaphor in mind: that of mapping. When mapping texts, the goal is not necessarily to find these 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 should prove useful not only to social scientists, but anyone interested in conducting text analysis projects

Data science · Haystack · Information retrieval · Linguistics · Metaphor · Workflow · World Wide Web · Advanced Text Analysis Techniques · Computational and Text Analysis Methods · Computer Science · Data Analysis with R

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Unique citing works10
Citations per year10
Citation span2025 - 2026 (2)
Citation velocitycurrent
Highly citedNo
Citation typesNeutral: 10

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