Mapping texts

• Verfasst von: Stoltz, Dustin S. [VerfasserIn]
• Verfasst von: Taylor, Marshall A. [VerfasserIn]
• Titel: Mapping texts
• Titelzusatz: computational text analysis for the social sciences
• Verf.angabe: Dustin S. Stoltz and Marshall A. Taylor
• Verlagsort: New York, NY
• Verlag: Oxford University Press
• E-Jahr: 2024
• Jahr: [2024]
• Umfang: XV, 307 Seiten
• Illustrationen: Diagramme
• Gesamttitel/Reihe: Computational social science
• Fussnoten: Literaturverzeichnis: Seite 281-301
• ISBN: 978-0-19-775688-1
• ISBN: 978-0-19-775687-4
• Sprache: eng
• Bibliogr. Hinweis: Erscheint auch als : Online-Ausgabe: Stoltz, Dustin S.: Mapping texts. - New York, NY : Oxford University Press, 2024. - 1 Online-Ressource (xvii, 307 Seiten)
• RVK-Notation: MR 2600
• RVK-Notation: MR 2200
• Sach-SW: Communication studies
• Sach-SW: Forschungsmethoden, allgemein
• Sach-SW: Kommunikationswissenschaft
• Sach-SW: LANGUAGE ARTS & DISCIPLINES / Communication
• Sach-SW: REFERENCE / Research
• Sach-SW: Research methods: general
• Sach-SW: SOCIAL SCIENCE / Research
• Sach-SW: SOCIAL SCIENCE / Statistics
• Sach-SW: Social research & statistics
• Sach-SW: Sozialforschung und -statistik
• K10plus-PPN: 1866005472

Abstract

"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"--

Abstract

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