The Computational Content Analyst : Using Machine Learning to Classify Media Messages
Most digital content, whether it be thousands of news articles or millions of social media posts, is too large for the naked eye alone. Often, the advent of immense datasets requires a more productive approach to labeling media beyond a team of researchers. This book offers practical guidance and Py...
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Routledge
2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=3961645&site=ehost-live header: @attributes: shortDbName: nlebk uiTerm: 3961645 longDbName: eBook Collection (EBSCOhost) uiTag: AN controlInfo: bkinfo: btl: The Computational Content Analyst : Using Machine Learning to Classify Media Messages aug: au: Chris J. Vargo isbn: 9781032846309 9781032846354 9781003514237 9781040227176 9781040227206 imageinfo: pubinfo: dt: @attributes: year: 2025 month: 01 day: 01 dtAvail: @attributes: year: 2024 month: 10 day: 15 pub: Routledge pubContract: Taylor & Francis (Unlimited) place: [S.l.] price: 0.01 limitsGroup: maxCheckoutDays: 1500 copyPages: -1 pda: N printPagesOffline: 60 printPagesOnline: 60 previewPages: 10000 prePubGroup: dewey: @attributes: class: 006.31 item: 006 .31 lc: @attributes: class: Q325.5 .V374 2025 item: Q 325 .5 .V374 2025 artinfo: ui: 3961645 1455747646 formats: fmt: – @attributes: type: EB doid: NL$3961645$PDF caption: PDF download: Y – @attributes: type: EK doid: NL$3961645$EPUB caption: EPUB download: Y tig: atl: The Computational Content Analyst : Using Machine Learning to Classify Media Messages ptl: The Computational Content Analyst aug: au: Chris J. Vargo su: Machine learning sug: subj: LANGUAGE ARTS & DISCIPLINES / Communication Studies SOCIAL SCIENCE / Methodology SOCIAL SCIENCE / Media Studies Machine learning ab: Most digital content, whether it be thousands of news articles or millions of social media posts, is too large for the naked eye alone. Often, the advent of immense datasets requires a more productive approach to labeling media beyond a team of researchers. This book offers practical guidance and Python code to traverse the vast expanses of data—significantly enhancing productivity without compromising scholarly integrity. We'll survey a wide array of computer-based classification approaches, focusing on easy-to-understand methodological explanations and best practices to ensure that your data is being labeled accurately and precisely. By reading this book, you should leave with an understanding of how to select the best computational content analysis methodology to your needs for the data and problem you have.This guide gives researchers the tools they need to amplify their analytical reach through the integration of content analysis with computational classification approaches, including machine learning and the latest advancements in generative artificial intelligence (AI) and large language models (LLMs). It is particularly useful for academic researchers looking to classify media data and advanced scholars in mass communications research, media studies, digital communication, political communication, and journalism.Complementing the book are online resources: datasets for practice, Python code scripts, extended exercise solutions, and practice quizzes for students, as well as test banks and essay prompts for instructors. Please visit www.routledge.com/9781032846354. pubtype: eBook doctype: Book ougenre: Book language: English copyright: @attributes: flag: N copyrightText: holdings: @attributes: islocal: N |
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