Computational Content Analyst - Using Machine Learning to Classify Media Messages

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.

List of contents

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

About the author

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.

Summary

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.

Product details

• Authors: Chris J. Vargo, Vargo Chris J.
• Publisher: Taylor & Francis Ltd.
• Languages: English
• Product format: Paperback / Softback
• Released: 02.12.2024
• EAN: 9781032846309
• ISBN: 978-1-0-3284630-9
• No. of pages: 134
• Subjects: Humanities, art, music > History; Natural sciences, medicine, IT, technology > Natural sciences (general); machine learning, Media Studies, History: specific events & topics, HISTORY / General, SOCIAL SCIENCE / Media Studies, LANGUAGE ARTS & DISCIPLINES / Communication Studies, REFERENCE / Research, Communication Studies, History: specific events and topics, COMPUTERS / Data Science / Machine Learning, Research methods: general