幫助中心 | 我的帳號 | 關於我們

挖掘社交網路(影印版第3版)(英文版)

  • 作者:(美)馬修·A.拉塞爾//米哈伊爾·克拉森
  • 出版社:東南大學
  • ISBN:9787564183738
  • 出版日期:2019/06/01
  • 裝幀:平裝
  • 頁數:400
人民幣:RMB 124 元      售價:
放入購物車
加入收藏夾

內容大鋼
    社交網站數據如同深埋地下的「金礦」,如何利用這些數據來發現哪些人正通過社交媒介進行聯繫?他們正在談論什麼?或者他們在哪兒?本書第2版對上一版內容進行了全面更新和修訂,它將揭示回答這些問題的方法與技巧。你將學到如何獲取、分析和匯總散落於社交網站(包括Facebook、Twitter、LinkedIn、Google+、 GitHub、郵件、網站和博客等)的數據,以及如何通過可視化找到你一直在社交世界中尋找的內容和你聞所未聞的有用信息。

作者介紹
(美)馬修·A.拉塞爾//米哈伊爾·克拉森

目錄
Preface
Part I.  A Guided Tour of the Social Web
Prelude
1. Mining Twitter: Exploring Trending Topics, Discovering What People Are Talking
    About, and More
    1.1 Overview
    1.2 Why Is Twitter All the Rage?
     1.3 Exploring Twitter's API
       1.3.1 Fundamental Twitter Terminology
       1.3.2 Creating a Twitter API Connection
       1.3.3 Exploring Trending Topics
       1.3.4 Searching for Tweets
     1.4 Analyzing the 140 (or More) Characters
       1.4.1 Extracting Tweet Entities
       1.4.2 Analyzing Tweets and Tweet Entities with Frequency Analysis
       1.4.3 Computing the Lexical Diversity of Tweets
       1.4.4 Examining Patterns in Retweets
       1.4.5 Visualizing Frequency Data with Histograms
     1.5 Closing Remarks
     1.6 Recommended Exercises
     1.7 Online Resources
2. Mining Facebook: Analyzing Fan Pages, Examining Friendships, and More
    2.1 Overview
   2.2 Exploring Facebook's Graph API
     2.2.1 Understanding the Graph API
     2.2.2 Understanding the Open Graph Protocol
   2.3 Analyzing Social Graph Connections
     2.3.1 Analyzing Facebook Pages
     2.3.2 Manipulating Data Using pandas
   2.4 Closing Remarks
   2.5 Recommended Exercises
   2.6 Online Resources
3. Mining Instagram: Computer Vision, Neural Networks, Object Recognition,
   and Face Detection
   3.1 Overview
   3.2 Exploring the Instagram API
     3.2.1 Making Instagram API Requests
     3.2.2 Retrieving Your Own Instagram Feed
     3.2.3 Retrieving Media by Hashtag
   3.3 Anatomy of an Instagram Post
   3.4 Crash Course on Artificial Neural Networks
     3.4.1 Training a Neural Network to "Look" at Pictures
     3.4.2 Recognizing Handwritten Digits
     3.4.3 Object Recognition Within Photos Using Pretrained Neural
       Networks
   3.5 Applying Neural Networks to Instagram Posts
     3.5.1 Tagging the Contents of an Image
     3.5.2 Detecting Faces in Images
   3.6 Closing Remarks
   3.7 Recommended Exercises

   3.8 Online Resources
4. Mining Linkeflln: Faceting Job Titles, Clustering Colleagues, and More
   4.1 Overview
   4.2 Exploring the LinkedIn API
     4.2.1 Making LinkedIn API Requests
     4.2.2 Downloading LinkedIn Connections as a CSV File
   4.3 Crash Course on Clustering Data
     4.3.1 Normalizing Data to Enable Analysis
     4.3.2 Measuring Similarity
     4.3.3 Clustering Algorithms
   4.4 Closing Remarks                                       /
   4.5 Recommended Exercises
   4.6 Online Resources
5. Mining Text Files: Computing Document Similarity, Extracting Collocations, and More.
   5.1 Overview
   5.2 Text Files
   5.3 A Whiz-Bang Introduction to TF-IDF
      5.3.1 Term Frequency
      5.3.2 Inverse Document Frequency
      5.3.3 TF-IDF
   5.4 Querying Human Language Data with TF-IDF
      5.4.1 Introducing the Natural Language Toolkit
      5.4.2 Applying TF-IDF to Human Language
      5.4.3 Finding Similar Documents
      5.4.4 Analyzing Bigrams in Human Language
      5.4.5 Reflections on Analyzing Human Language Data
    5.5 Closing Remarks
    5.6 Recommended Exercises
    5.7 Online Resources
6. Mining Web Pages: Using Natural Language Processing to Understand Human
    Language, Summarize Blog Posts, and More
    6.1 Overview
    6.2 Scraping, Parsing, and Crawling the Web
      6.2.1 Breadth-First Search in Web Crawling
    6.3 Discovering Semantics by Decoding Syntax
      6.3.1 Natural Language Processing Illustrated Step-by-Step
      6.3.2 Sentence Detection in Human Language Data
      6.3.3 Document Summarization
    6.4 Entity-Centric Analysis: A Paradigm Shift
      6.4.1 Gisting Human Language Data
    6.5 Quality of Analytics for Processing Human Language Data
    6.6 Closing Remarks
    6.7 Recommended Exercises
    6.8 Online Resources
7. Mining Mailboxes: Analyzing Who's Talking to Whom About What,
    How Often, and More
    7.1 Overview
    7.2 Obtaining and Processing a Mail Corpus
      7.2.1 A Primer on Unix Mailboxes
      7.2.2 Getting the Enron Data

      7.2.3 Converting a Mail Corpus to a Unix Mailbox
      7.2.4 Converting Unix Mailboxes to pandas DataFrames
     7.3 Analyzing the Enron Corpus
       7.3.1 Querying by Date/Time Range
       7.3.2 Analyzing Patterns in Sender/Recipient Communications
       7.3.3 Searching Emails by Keywords
     7.4 Analyzing Your Own Mail Data
       7.4.1 Accessing Your Gmail with OAuth
       7.4.2 Fetching and Parsing Email Messages
       7.4.3 Visualizing Patterns in Email with Immersion
     7.5 Closing Remarks
     7.6 Recommended Exercises
     7.7 Online Resources
8. Mining GitHub: Inspecting Software Collaboration Habits, Building Interest Graphs,
     and More
     8.1 Overview
     8.2 Exploring GitHub's API
       8.2.1 Creating a GitHub API Connection
       8.2.2 Making GitHub API Requests
     8.3 Modeling Data with Property Graphs
     8.4 Analyzing GitHub Interest Graphs
       8.4.1 Seeding an Interest Graph
       8.4.2 Computing Graph Centrality Measures
       8.4.3 Extending the Interest Graph with "Follows" Edges for Users
       8.4.4 Using Nodes as Pivots for More Efficient Queries
       8.4.5 Visualizing Interest Graphs
    8.5 Closing Remarks
    8.6 Recommended Exercises
    8.7 Online Resources
Part II.  Twitter Cookbook
9. Twitter Cookbook
    9.1 Accessing Twitter's API for Development Purposes
    9.2 Doing the OAuth Dance to Access Twitter's API for Production Purposes
    9.3 Discovering the Trending Topics
    9.4 Searching for Tweets
    9.5 Constructing Convenient Function Calls
    9.6 Saving and Restoring ]SON Data with Text Files
    9.7 Saving and Accessing JSON Data with MongoDB               /
    9.8 Sampling the Twitter Firehose with the Streaming API
    9.9 Collecting Time-Series Data
     9.10 Extracting Tweet Entities
     9.11 Finding the Most Popular Tweets in a Collection of Tweets
     9.12 Finding the Most Popular Tweet Entities in a Collection of Tweets
     9.13 Tabulating Frequency Analysis
     9.14 Finding Users Who Have Retweeted a Status
     9.15 Extracting a Retweet's Attribution
     9.16 Making Robust Twitter Requests
     9.17 Resolving User Profile Information
     9.18 Extracting Tweet Entities from Arbitrary Text
     9.19 Getting All Friends or Followers for a User

     9.20 Analyzing a User's Friends and Followers
     9.21 Harvesting a User's Tweets
     9.22 Crawling a Friendship Graph
     9.23 Analyzing Tweet Content
     9.24 Summarizing Link Targets
     9.25 Analyzing a User's Favorite Tweets
     9.26 Closing Remarks
     9.27 Recommended Exercises
     9.28 Online Resources
Part III.  Appendixes
  A. Information About This Book's Virtual Machine Experience
  B. OAuth Primer
  C. Python and Jupyter Notebook Tips and Tricks
Index

  • 商品搜索:
  • | 高級搜索
首頁新手上路客服中心關於我們聯絡我們Top↑
Copyrightc 1999~2008 美商天龍國際圖書股份有限公司 臺灣分公司. All rights reserved.
營業地址:臺北市中正區重慶南路一段103號1F 105號1F-2F
讀者服務部電話:02-2381-2033 02-2381-1863 時間:週一-週五 10:00-17:00
 服務信箱:bookuu@69book.com 客戶、意見信箱:cs@69book.com
ICP證:浙B2-20060032