The Computational Content Analyst: Using Machine Learning to Classify Media Messages
Author: Chris J. Vargo (Author)
Publisher finelybook 出版社: Routledge
Edition 版本: 1st
Publication Date 出版日期: 2024-12-13
Language 语言: English
Print Length 页数: 134 pages
ISBN-10: 1032846305
ISBN-13: 9781032846309
Book Description
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 http://www.routledge.com/9781032846354.
Review
“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
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.
相关文件下载地址
相关推荐
Hands-On Machine Learning with C++: Build, train, and deploy end-to-end machine learning and deep learning pipelines, 2nd Edition- IDS and IPS with Snort 3: Get up and running with Snort 3 and discover effective solutions to your security issues
- Microsoft 365 Administration Cookbook: Enhance your Microsoft 365 productivity to manage and optimize its apps and services, 2nd Edition
- Zabbix 7 IT Infrastructure Monitoring Cookbook: Explore the new features of Zabbix 7 for designing, building, and maintaining your Zabbix setup, 3rd Edition
- Refactoring with C++: Explore modern ways of developing maintainable and efficient applications
- Microsoft 365 Copilot At Work: Using AI to Get the Most from Your Business Data and Favorite Apps
