Chris J. Vargo

The Computational Content Analyst (eBook, PDF)

Using Machine Learning to Classify Media Messages

• Format: PDF

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
• Seitenzahl: 144
• Erscheinungstermin: 2. Dezember 2024
• Englisch
• ISBN-13: 9781040227176
• Artikelnr.: 72277256

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