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Mining the Social Web. Data Mining Facebook, Twitter, LinkedIn, Instagram, GitHub, and More. 3rd Edition - Helion

Mining the Social Web. Data Mining Facebook, Twitter, LinkedIn, Instagram, GitHub, and More. 3rd Edition
ebook
Autor: Matthew A. Russell, Mikhail Klassen
ISBN: 978-14-919-7350-9
stron: 428, Format: ebook
Data wydania: 2018-12-04
Księgarnia: Helion

Cena książki: 152,15 zł (poprzednio: 176,92 zł)
Oszczędzasz: 14% (-24,77 zł)

Dodaj do koszyka Mining the Social Web. Data Mining Facebook, Twitter, LinkedIn, Instagram, GitHub, and More. 3rd Edition

Mine the rich data tucked away in popular social websites such as Twitter, Facebook, LinkedIn, 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

Dodaj do koszyka Mining the Social Web. Data Mining Facebook, Twitter, LinkedIn, Instagram, GitHub, and More. 3rd Edition

 

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Dodaj do koszyka Mining the Social Web. Data Mining Facebook, Twitter, LinkedIn, Instagram, GitHub, and More. 3rd Edition

Spis treści

Mining the Social Web. Data Mining Facebook, Twitter, LinkedIn, Instagram, GitHub, and More. 3rd Edition eBook -- spis treści

  • Preface
    • A Note from Matthew Russell
    • README.1st
    • Managing Your Expectations
    • Python-Centric Technology
    • Improvements to the Third Edition
    • The Ethical Use of Data Mining
    • Conventions Used in This Book
    • Using Code Examples
    • OReilly Safari
    • How to Contact Us
    • Acknowledgments for the Third Edition
    • Acknowledgments for the Second Edition
    • Acknowledgments from the First Edition
  • I. A Guided Tour of the Social Web
  • Prelude
  • 1. Mining Twitter: Exploring Trending Topics, Discovering What People Are Talking About, and More
    • Overview
    • Why Is Twitter All the Rage?
    • Exploring Twitters API
      • Fundamental Twitter Terminology
      • Creating a Twitter API Connection
      • Exploring Trending Topics
      • Searching for Tweets
    • Analyzing the 140 (or More) Characters
      • Extracting Tweet Entities
      • Analyzing Tweets and Tweet Entities with Frequency Analysis
      • Computing the Lexical Diversity of Tweets
      • Examining Patterns in Retweets
      • Visualizing Frequency Data with Histograms
    • Closing Remarks
    • Recommended Exercises
    • Online Resources
  • 2. Mining Facebook: Analyzing Fan Pages, Examining Friendships, and More
    • Overview
    • Exploring Facebooks Graph API
      • Understanding the Graph API
      • Understanding the Open Graph Protocol
    • Analyzing Social Graph Connections
      • Analyzing Facebook Pages
        • Analyzing this books Facebook page
        • Engaging fans and measuring the strength of a social media brand
      • Manipulating Data Using pandas
        • Visualizing audience engagement using matplotlib
        • Calculating mean engagement
    • Closing Remarks
    • Recommended Exercises
    • Online Resources
  • 3. Mining Instagram: Computer Vision, Neural Networks, Object Recognition, and Face Detection
    • Overview
    • Exploring the Instagram API
      • Making Instagram API Requests
      • Retrieving Your Own Instagram Feed
      • Retrieving Media by Hashtag
    • Anatomy of an Instagram Post
    • Crash Course on Artificial Neural Networks
      • Training a Neural Network to Look at Pictures
      • Recognizing Handwritten Digits
      • Object Recognition Within Photos Using Pretrained Neural Networks
    • Applying Neural Networks to Instagram Posts
      • Tagging the Contents of an Image
      • Detecting Faces in Images
    • Closing Remarks
    • Recommended Exercises
    • Online Resources
  • 4. Mining LinkedIn: Faceting Job Titles, Clustering Colleagues, and More
    • Overview
    • Exploring the LinkedIn API
      • Making LinkedIn API Requests
      • Downloading LinkedIn Connections as a CSV File
    • Crash Course on Clustering Data
      • Normalizing Data to Enable Analysis
        • Normalizing and counting companies
        • Normalizing and counting job titles
        • Normalizing and counting locations
        • Visualizing locations with cartograms
      • Measuring Similarity
      • Clustering Algorithms
        • Greedy clustering
          • Runtime analysis
        • Hierarchical clustering
        • k-means clustering
        • Visualizing geographic clusters with Google Earth
    • Closing Remarks
    • Recommended Exercises
    • Online Resources
  • 5. Mining Text Files: Computing Document Similarity, Extracting Collocations, and More
    • Overview
    • Text Files
    • A Whiz-Bang Introduction to TF-IDF
      • Term Frequency
      • Inverse Document Frequency
      • TF-IDF
    • Querying Human Language Data with TF-IDF
      • Introducing the Natural Language Toolkit
      • Applying TF-IDF to Human Language
      • Finding Similar Documents
        • The theory behind vector space models and cosine similarity
        • Clustering posts with cosine similarity
        • Visualizing document similarity with a matrix diagram
      • Analyzing Bigrams in Human Language
        • Contingency tables and scoring functions
      • Reflections on Analyzing Human Language Data
    • Closing Remarks
    • Recommended Exercises
    • Online Resources
  • 6. Mining Web Pages: Using Natural Language Processing to Understand Human Language, Summarize Blog Posts, and More
    • Overview
    • Scraping, Parsing, and Crawling the Web
      • Breadth-First Search in Web Crawling
    • Discovering Semantics by Decoding Syntax
      • Natural Language Processing Illustrated Step-by-Step
      • Sentence Detection in Human Language Data
      • Document Summarization
        • Analysis of Luhns summarization algorithm
    • Entity-Centric Analysis: A Paradigm Shift
      • Gisting Human Language Data
    • Quality of Analytics for Processing Human Language Data
    • Closing Remarks
    • Recommended Exercises
    • Online Resources
  • 7. Mining Mailboxes: Analyzing Whos Talking to Whom About What, How Often, and More
    • Overview
    • Obtaining and Processing a Mail Corpus
      • A Primer on Unix Mailboxes
      • Getting the Enron Data
      • Converting a Mail Corpus to a Unix Mailbox
      • Converting Unix Mailboxes to pandas DataFrames
    • Analyzing the Enron Corpus
      • Querying by Date/Time Range
      • Analyzing Patterns in Sender/Recipient Communications
      • Searching Emails by Keywords
    • Analyzing Your Own Mail Data
      • Accessing Your Gmail with OAuth
      • Fetching and Parsing Email Messages
      • Visualizing Patterns in Email with Immersion
    • Closing Remarks
    • Recommended Exercises
    • Online Resources
  • 8. Mining GitHub: Inspecting Software Collaboration Habits, Building Interest Graphs, and More
    • Overview
    • Exploring GitHubs API
      • Creating a GitHub API Connection
      • Making GitHub API Requests
    • Modeling Data with Property Graphs
    • Analyzing GitHub Interest Graphs
      • Seeding an Interest Graph
      • Computing Graph Centrality Measures
      • Extending the Interest Graph with Follows Edges for Users
        • Application of centrality measures
        • Adding more repositories to the interest graph
        • Computational considerations
      • Using Nodes as Pivots for More Efficient Queries
      • Visualizing Interest Graphs
    • Closing Remarks
    • Recommended Exercises
    • Online Resources
  • II. Twitter Cookbook
  • 9. Twitter Cookbook
    • Accessing Twitters API for Development Purposes
      • Problem
      • Solution
      • Discussion
    • Doing the OAuth Dance to Access Twitters API for Production Purposes
      • Problem
      • Solution
      • Discussion
    • Discovering the Trending Topics
      • Problem
      • Solution
      • Discussion
    • Searching for Tweets
      • Problem
      • Solution
      • Discussion
    • Constructing Convenient Function Calls
      • Problem
      • Solution
      • Discussion
    • Saving and Restoring JSON Data with Text Files
      • Problem
      • Solution
      • Discussion
    • Saving and Accessing JSON Data with MongoDB
      • Problem
      • Solution
      • Discussion
    • Sampling the Twitter Firehose with the Streaming API
      • Problem
      • Solution
      • Discussion
    • Collecting Time-Series Data
      • Problem
      • Solution
      • Discussion
    • Extracting Tweet Entities
      • Problem
      • Solution
      • Discussion
    • Finding the Most Popular Tweets in a Collection of Tweets
      • Problem
      • Solution
      • Discussion
    • Finding the Most Popular Tweet Entities in a Collection of Tweets
      • Problem
      • Solution
      • Discussion
    • Tabulating Frequency Analysis
      • Problem
      • Solution
      • Discussion
    • Finding Users Who Have Retweeted a Status
      • Problem
      • Solution
      • Discussion
    • Extracting a Retweets Attribution
      • Problem
      • Solution
      • Discussion
    • Making Robust Twitter Requests
      • Problem
      • Solution
      • Discussion
    • Resolving User Profile Information
      • Problem
      • Solution
      • Discussion
    • Extracting Tweet Entities from Arbitrary Text
      • Problem
      • Solution
      • Discussion
    • Getting All Friends or Followers for a User
      • Problem
      • Solution
      • Discussion
    • Analyzing a Users Friends and Followers
      • Problem
      • Solution
      • Discussion
    • Harvesting a Users Tweets
      • Problem
      • Solution
      • Discussion
    • Crawling a Friendship Graph
      • Problem
      • Solution
      • Discussion
    • Analyzing Tweet Content
      • Problem
      • Solution
      • Discussion
    • Summarizing Link Targets
      • Problem
      • Solution
      • Discussion
    • Analyzing a Users Favorite Tweets
      • Problem
      • Solution
      • Discussion
    • Closing Remarks
    • Recommended Exercises
    • Online Resources
  • III. Appendixes
  • A. Information About This Books Virtual Machine Experience
  • B. OAuth Primer
    • Overview
      • OAuth 1.0a
      • OAuth 2.0
  • C. Python and Jupyter Notebook Tips and Tricks
  • Index

Dodaj do koszyka Mining the Social Web. Data Mining Facebook, Twitter, LinkedIn, Instagram, GitHub, and More. 3rd Edition

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