The Computational Content Analyst: Using Machine Learning to Classify Media Messages

Vargo, Chris J.

  • 出版商: Routledge
  • 出版日期: 2024-12-02
  • 售價: $2,070
  • 貴賓價: 9.5$1,967
  • 語言: 英文
  • 頁數: 134
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1032846305
  • ISBN-13: 9781032846309
  • 相關分類: Machine Learning
  • 尚未上市,無法訂購

相關主題

商品描述

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.

商品描述(中文翻譯)

大多數數位內容,無論是數千篇新聞文章還是數百萬條社交媒體帖子,對肉眼來說都過於龐大。通常,龐大數據集的出現需要一種比單靠研究團隊更具生產力的標記媒體的方法。本書提供實用的指導和 Python 代碼,以便在廣闊的數據中穿梭——顯著提高生產力而不損害學術誠信。我們將調查各種基於計算機的分類方法,重點關注易於理解的方法論解釋和最佳實踐,以確保您的數據被準確且精確地標記。閱讀本書後,您應該能夠理解如何根據您的數據和問題選擇最適合的計算內容分析方法論。

本指南為研究人員提供了所需的工具,通過將內容分析與計算分類方法(包括機器學習以及最新的生成式人工智慧和大型語言模型)相結合,擴大他們的分析範圍。這對於希望對媒體數據進行分類的學術研究者以及在大眾傳播研究、媒體研究、數位傳播、政治傳播和新聞學領域的高級學者特別有用。

本書還附有線上資源:供練習用的數據集、Python 代碼腳本、擴展練習解答和學生練習測驗,以及供教師使用的測試題庫和論文題目。請訪問 www.routledge.com/9781032846354。

作者簡介

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.

作者簡介(中文翻譯)

Chris J. Vargo 是美國科羅拉多大學博爾德分校媒體、傳播與資訊學院及利茲商學院的副教授(兼任)。他的研究主要集中在計算媒體分析與政治傳播的交集,運用計算方法來增進這些領域的理解。