计算内容分析师：使用机器学习对媒体信息进行分类
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.
英文| 2024 |国际标准图书编号：9781003514237 | 144页|真PDF，EPUB | 4.09 MB 大多数数字内容，无论是数千篇新闻文章还是数百万条社交媒体帖子，单凭肉眼都太大了。通常，巨大数据集的出现需要一种更高效的方法来标记媒体，而不仅仅是一个研究团队。这本书提供了实用的指导和Python代码来遍历海量的数据，在不损害学术诚信的情况下显著提高了生产力。我们将调查各种基于计算机的分类方法，重点关注易于理解的方法解释和最佳实践，以确保您的数据被准确和精确地标记。通过阅读本书，您应该了解如何根据您对数据和问题的需求选择最佳的计算内容分析方法。 本指南为研究人员提供了所需的工具，通过将内容分析与计算分类方法相结合来扩大他们的分析范围，包括机器学习和生成人工智能（AI）和大型语言模型（LLM）的最新进展。它对于希望对媒体数据进行分类的学术研究人员和大众传播研究、媒体研究、数字传播、政治传播和新闻学领域的高级学者特别有用。 补充这本书的是在线资源：练习数据集、Python代码脚本、扩展练习解决方案、学生练习测验，以及教师的题库和论文提示。请访问www.routeledge.com/9781032846354。本站不对文件进行储存，仅提供文件链接，请自行下载，本站不对文件内容负责，请自行判断文件是否安全，如发现文件有侵权行为，请联系管理员删除。
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