Mining the Social Web 3rd Edition by Mikhail Klassen, Matthew A Russell ISBN 1491985046 9781491985045

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ISBN 10: 1491985046 
ISBN 13: 9781491985045
Author: Mikhail Klassen, Matthew A Russell

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

Mining the Social Web 3rd Table of contents:

Part I: Mining Social Data from Specific Platforms

• Chapter 1: Getting Started with Social Data
  • What is Social Data?
  • The Power of Social Media APIs
  • Setting Up Your Development Environment (Python, Libraries, Docker)
  • Ethical Considerations and Best Practices
• Chapter 2: Mining Twitter Data
  • Accessing the Twitter API (OAuth, API Keys)
  • Searching for Tweets
  • Streaming Data from the Twitter Firehose
  • Extracting Tweet Entities (hashtags, mentions, URLs)
  • Analyzing User Relationships (friends, followers)
• Chapter 3: Mining Facebook Data
  • Understanding the Facebook Graph API
  • Authentication and Permissions
  • Extracting Public Posts, Comments, and Likes
  • Analyzing User Profiles and Connections
  • Group and Page Insights
• Chapter 4: Mining LinkedIn Data
  • LinkedIn API Access (Developer Program)
  • Extracting Profile Information
  • Analyzing Connections and Network Graphs
  • Company Data and Industry Trends
• Chapter 5: Mining Instagram Data
  • Accessing the Instagram Graph API (and changes over time)
  • Extracting Posts, Captions, and Hashtags
  • Image Recognition and Analysis (integrating with ML libraries)
  • User Engagement Metrics
• Chapter 6: Mining GitHub Data
  • Using the GitHub API
  • Extracting Repository Information, Commits, and Collaborators
  • Analyzing Developer Networks and Open Source Contributions
  • Identifying Influential Users and Projects
• Chapter 7: Mining Web Pages, Blogs, and Feeds
  • Web Scraping Fundamentals (BeautifulSoup, Requests)
  • RSS/Atom Feed Parsing
  • Extracting Content from Various Web Sources
  • Handling Common Web Scraping Challenges (pagination, CAPTCHAs)
• Chapter 8: Mining Mailboxes and Other Data Sources
  • Accessing Email Data (Gmail API, IMAP)
  • Analyzing Email Communication Patterns
  • Integrating with Other Data Sources (e.g., public datasets)

Part II: Advanced Data Mining and Analysis Techniques

• Chapter 9: Exploring Data with Python
  • Numpy and Pandas for Data Manipulation
  • Data Cleaning and Preprocessing
  • Exploratory Data Analysis (EDA)
  • Introduction to Data Visualization (Matplotlib, Seaborn)
• Chapter 10: Natural Language Processing (NLP) for Social Data
  • Tokenization, Stemming, and Lemmatization
  • Part-of-Speech Tagging
  • Named Entity Recognition (NER)
  • Sentiment Analysis
  • Topic Modeling (Latent Dirichlet Allocation – LDA)
• Chapter 11: Graph Theory and Network Analysis
  • Representing Social Data as Graphs
  • Centrality Measures (degree, betweenness, closeness)
  • Community Detection Algorithms (Louvain, Girvan-Newman)
  • Visualizing Social Networks (NetworkX, Gephi)
• Chapter 12: Machine Learning for Social Data
  • Supervised Learning (Classification: Spam Detection, User Classification)
  • Unsupervised Learning (Clustering: User Segmentation)
  • Feature Engineering for Social Data
  • Introduction to Deep Learning for Text and Images
• Chapter 13: Image and Multimedia Analysis
  • Fundamentals of Image Processing
  • Object Detection and Recognition (using pre-trained models)
  • Facial Recognition and Analysis
  • Extracting Metadata from Images
• Chapter 14: Time-Series Analysis and Trend Detection
  • Analyzing Temporal Patterns in Social Data
  • Identifying Trending Topics and Events
  • Forecasting Social Media Activity
• Chapter 15: Building Beautiful Data Visualizations
  • Advanced Visualization Techniques with Python (Plotly, Bokeh)
  • Interactive Visualizations with JavaScript Libraries (D3.js)
  • Storytelling with Data Visualizations

Part III: Applications and Ethical Considerations

• Chapter 16: Practical Applications of Social Data Mining
  • Marketing and Brand Monitoring
  • Customer Service and Support
  • Public Opinion and Sentiment Tracking
  • Fraud Detection and Security
  • Social Science Research
• Chapter 17: Ethical and Legal Considerations
  • Privacy Concerns and Data Anonymization
  • Terms of Service and API Usage Policies
  • Data Security and Responsible Data Handling
  • Bias in Data and Algorithms
• Chapter 18: The Future of Social Data Mining
  • Emerging Platforms and Technologies
  • Advancements in AI and Machine Learning for Social Data
  • Challenges and Opportunities

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Tags: Mikhail Klassen, Matthew A Russell, Mining, Social