Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More, 3/e (Paperback)

Matthew A. Russell, Mikhail Klassen

Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More, 3/e (Paperback)

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商品描述

Mine the rich data tucked away in popular social websites such as Twitter, Facebook, LinkedIn, Google+, and Instagram. With the third edition of this popular guide, data scientists, analysts, and programmers will learn how to glean insights from social media—including who’s connecting with whom, what they’re talking about, and where they’re located—using Python code examples, Jupyter notebooks, or Docker containers.

In part one, each standalone chapter focuses on one aspect of the social landscape, including each of the major social sites, as well as web pages, blogs and feeds, mailboxes, GitHub, and a newly added chapter covering Instagram. Part two provides a cookbook with two dozen bite-size recipes for solving particular issues with Twitter.

  • Get a straightforward synopsis of the social web landscape
  • Use Docker to easily run each chapter’s example code, packaged as a Jupyter notebook
  • Adapt and contribute to the code’s open source GitHub repository
  • Learn how to employ best-in-class Python 3 tools to slice and dice the data you collect
  • Apply advanced mining techniques such as TFIDF, cosine similarity, collocation analysis, clique detection, and image recognition
  • Build beautiful data visualizations with Python and JavaScript toolkits

商品描述(中文翻譯)

挖掘存放在流行社交網站（如Twitter、Facebook、LinkedIn、Google+和Instagram）中的豐富數據。通過本書的第三版，數據科學家、分析師和程序員將學習如何使用Python代碼示例、Jupyter筆記本或Docker容器從社交媒體中獲取洞察力，包括誰與誰聯繫、他們在談論什麼以及他們的位置。

第一部分的每個獨立章節都專注於社交媒體的一個方面，包括每個主要的社交網站，以及網頁、博客和訂閱源、郵箱、GitHub，還新增了一個章節介紹Instagram。第二部分提供了一本食譜，其中包含兩打個小型的Twitter問題解決方案。

- 獲取社交網絡景觀的簡明概述
- 使用Docker輕鬆運行每個章節的示例代碼，以Jupyter筆記本的形式打包
- 適應並貢獻代碼的開源GitHub存儲庫
- 學習如何使用最佳Python 3工具來切割和分析收集到的數據
- 應用高級挖掘技術，如TFIDF、餘弦相似度、共現分析、圈子檢測和圖像識別
- 使用Python和JavaScript工具包建立美麗的數據可視化

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