Mining the Social Web, 2E
暫譯: 挖掘社交網路, 第二版
Matthew A. Russell
- 出版商: O'Reilly
- 出版日期: 2013-10-20
- 定價: $1,575
- 售價: 5.0 折 $788
- 語言: 英文
- 頁數: 448
- 裝訂: Paperback
- ISBN: 1449367615
- ISBN-13: 9781449367619
-
相關分類:
Text-mining
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商品描述
How can you tap into the wealth of social web data to discover who’s making connections with whom, what they’re talking about, and where they’re located? With this expanded and thoroughly revised edition, you’ll learn how to acquire, analyze, and summarize data from all corners of the social web, including Facebook, Twitter, LinkedIn, Google+, GitHub, email, websites, and blogs.
- Employ the Natural Language Toolkit, NetworkX, and other scientific computing tools to mine popular social web sites
- Apply advanced text-mining techniques, such as clustering and TF-IDF, to extract meaning from human language data
- Bootstrap interest graphs from GitHub by discovering affinities among people, programming languages, and coding projects
- Build interactive visualizations with D3.js, an extraordinarily flexible HTML5 and JavaScript toolkit
- Take advantage of more than two-dozen Twitter recipes, presented in O’Reilly’s popular "problem/solution/discussion" cookbook format
The example code for this unique data science book is maintained in a public GitHub repository. It’s designed to be easily accessible through a turnkey virtual machine that facilitates interactive learning with an easy-to-use collection of IPython Notebooks.
商品描述(中文翻譯)
如何利用社交網路數據的豐富資源來發現誰與誰建立了聯繫、他們在談論什麼以及他們的位置在哪裡?在這本擴展且徹底修訂的版本中,您將學習如何從社交網路的各個角落獲取、分析和總結數據,包括 Facebook、Twitter、LinkedIn、Google+、GitHub、電子郵件、網站和部落格。
- 使用自然語言工具包(Natural Language Toolkit)、NetworkX 及其他科學計算工具來挖掘熱門社交網站
- 應用先進的文本挖掘技術,如聚類(clustering)和 TF-IDF,從人類語言數據中提取意義
- 從 GitHub 中建立興趣圖,通過發現人員、程式語言和編碼專案之間的親和性
- 使用 D3.js 建立互動式視覺化,這是一個極具靈活性的 HTML5 和 JavaScript 工具包
- 利用 O'Reilly 受歡迎的「問題/解決方案/討論」食譜格式,掌握超過二十種 Twitter 食譜
這本獨特的數據科學書籍的範例代碼保存在公共的 GitHub 倉庫中。它設計為可通過即開即用的虛擬機輕鬆訪問,並提供易於使用的 IPython Notebooks 集合以促進互動學習。
