Chris J. Vargo

The Computational Content Analyst

Using Machine Learning to Classify Media Messages

• Broschiertes Buch

Produktbeschreibung

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 AI and Large Language Models (LLMs). It is particularly useful for academic researchers and students.

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Produktdetails

• Verlag: Taylor & Francis Ltd
• Seitenzahl: 134
• Erscheinungstermin: 2. Dezember 2024
• Englisch
• Abmessung: 227mm x 152mm x 10mm
• Gewicht: 218g
• ISBN-13: 9781032846309
• ISBN-10: 1032846305
• Artikelnr.: 71183733

Autorenporträt

Chris J. Vargo is an Associate Professor in the College of Media, Communication, and Information and Leeds School of Business (Courtesy) at the University of Colorado Boulder, USA. His research primarily focuses on the intersection of computational media analytics and political communication, employing computational methods to enhance understanding in these areas.

Inhaltsangabe

Preface 1. Unveiling Content Analysis in the Contemporary Media Ecosystem
2. Designing a Computational Content Analysis: An Illustration from "Civic
Engagement, Social Capital, and Ideological Extremity" 3. Basic Information
Retrieval for Content Analysis 4. Supervised Machine Learning with BERT for
Content Analysis 5. Text Classification of News Media Content Categories
Using Deep Learning 6. Leveraging Generative AI for Content Analysis 7.
Unveiling the Veiled: Topic Modeling as a Lens for Discovery 8. Extending
Deep Learning to Image Content Analysis Appendix A: Codebook and Conceptual
Definitions Appendix B: Deletion Themes

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Preface 1. Unveiling Content Analysis in the Contemporary Media Ecosystem 2. Designing a Computational Content Analysis: An Illustration from "Civic Engagement, Social Capital, and Ideological Extremity" 3. Basic Information Retrieval for Content Analysis 4. Supervised Machine Learning with BERT for Content Analysis 5. Text Classification of News Media Content Categories Using Deep Learning 6. Leveraging Generative AI for Content Analysis 7. Unveiling the Veiled: Topic Modeling as a Lens for Discovery 8. Extending Deep Learning to Image Content Analysis Appendix A: Codebook and Conceptual Definitions Appendix B: Deletion Themes

Rezensionen

"The Computational Content Analyst opens new research frontiers using highly sophisticated computer-based approaches that greatly expand the substantive depth and scope of quantitative content analysis. These approaches vastly improve scholars' ability to examine the large body of content available on the internet."

- Maxwell McCombs, University of Texas at Austin, USA

"The Computational Content Analyst provides a practical and informative guide for scholars and practitioners aiming to learn the basics of computational approaches to analyzing text. This book is practical and insightful; Vargo makes a complex topic accessible through insightful examples and useful research case studies."

- Matthew Weber, Rutgers University, USA

"This book makes computational content analysis as easy as following a recipe."

- Milad Minooie, Kennesaw State University, USA

"The Computational Content Analyst opens new research frontiers using highly sophisticated computer-based approaches that greatly expand the substantive depth and scope of quantitative content analysis. These approaches vastly improve scholars' ability to examine the large body of content available on the internet."

- Maxwell McCombs, University of Texas at Austin, USA

"The Computational Content Analyst provides a practical and informative guide for scholars and practitioners aiming to learn the basics of computational approaches to analyzing text. This book is practical and insightful; Vargo makes a complex topic accessible through insightful examples and useful research case studies."

- Matthew Weber, Rutgers University, USA

"This book makes computational content analysis as easy as following a recipe."

- Milad Minooie, Kennesaw State University, USA