Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More, 3/e (Paperback)
暫譯: 挖掘社交網路：數據挖掘 Facebook、Twitter、LinkedIn、Google+、GitHub 等平台，第 3 版 (平裝本)
Matthew A. Russell, Mikhail Klassen
- 出版商: O'Reilly
- 出版日期: 2019-02-12
- 定價: $1,980
- 售價: 8.5 折 $1,683
- 語言: 英文
- 頁數: 432
- 裝訂: Paperback
- ISBN: 1491985046
- ISBN-13: 9781491985045
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相關分類:
Data-mining
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相關翻譯:
社群網站的資料探勘, 3/e (Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More, 3/e) (繁中版)
社交網站的數據挖掘與分析（原書第3版） (簡中版)
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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)
挖掘隱藏在流行社交網站如 Twitter、Facebook、LinkedIn、Google+ 和 Instagram 中的豐富數據。透過這本受歡迎指南的第三版，數據科學家、分析師和程式設計師將學會如何從社交媒體中提取見解——包括誰在與誰連接、他們在談論什麼以及他們的位置——使用 Python 代碼範例、Jupyter 筆記本或 Docker 容器。
在第一部分中，每個獨立的章節專注於社交環境的某一方面，包括每個主要社交網站，以及網頁、部落格和資訊流、郵箱、GitHub，還有一個新增的章節涵蓋 Instagram。第二部分提供了一本食譜，包含二十多個針對 Twitter 特定問題的簡短解決方案。
- 獲得社交網絡環境的簡明概述
- 使用 Docker 輕鬆運行每個章節的示例代碼，打包為 Jupyter 筆記本
- 調整並貢獻於代碼的開源 GitHub 倉庫
- 學習如何使用一流的 Python 3 工具來切割和處理您收集的數據
- 應用先進的挖掘技術，如 TFIDF、餘弦相似度、搭配分析、社群檢測和圖像識別
- 使用 Python 和 JavaScript 工具包構建美觀的數據可視化
