Title
Mining The Social Web: Data Mining Facebook, Twitter, Linkedin, Instagram, Github, And More
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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 Mediaincluding Whos Connecting With Whom, What Theyre Talking About, And Where Theyre Locatedusing 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 Bitesize Recipes For Solving Particular Issues With Twitter. Get A Straightforward Synopsis Of The Social Web Landscape Use Docker To Easily Run Each Chapters Example Code, Packaged As A Jupyter Notebook Adapt And Contribute To The Codes Open Source Github Repository Learn How To Employ Bestinclass 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
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This product may contain chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm.
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- Q: How many pages does this book have? A: This book has four hundred twenty-six pages. It's a comprehensive guide that delivers extensive insights into data mining social media.
- Q: What is the binding type of this book? A: This book is a paperback edition. This makes it lightweight and portable, ideal for reading on the go.
- Q: What are the dimensions of this book? A: The dimensions of this book are seven point zero one inches in length, zero point eight inches in width, and nine point one inches in height. This size makes it easy to fit on most bookshelves.
- Q: Who is the author of this book? A: The author of this book is Matthew A. Russell. He is known for his expertise in data science and social media.
- Q: What category does this book belong to? A: This book belongs to the Business Technology category. It focuses on data mining techniques relevant to social media platforms.
- Q: How can I use this book for data mining? A: You can use this book to learn data mining techniques through Python code examples and Jupyter notebooks. It provides practical recipes for solving specific issues with social media data.
- Q: Is this book suitable for beginners? A: Yes, this book is suitable for beginners. It starts with fundamental concepts and gradually advances to more complex data mining techniques.
- Q: Are there practical exercises in this book? A: Yes, there are practical exercises included in each chapter. These exercises help reinforce the concepts and techniques discussed throughout the book.
- Q: Can I run the example code on Docker? A: Yes, you can run the example code using Docker. Each chapter’s example code is packaged as a Jupyter notebook for easy execution.
- Q: What advanced techniques are covered in the book? A: The book covers advanced techniques such as TFIDF, cosine similarity, collocation analysis, clique detection, and image recognition. These methods enhance data analysis capabilities.
- Q: How do I care for this book? A: To care for this book, keep it in a dry, cool place and avoid direct sunlight. This will help maintain its condition over time.
- Q: Is this book safe for children? A: This book is not specifically targeted at children. It's best suited for adults and professionals in data science or technology fields.
- Q: What if I receive a damaged book? A: If you receive a damaged book, contact the seller for return or replacement options. Most retailers have policies in place for such issues.
- Q: Does this book have a warranty? A: No, books typically do not come with a warranty. However, check the retailer's return policy for specifics on damaged or unsatisfactory items.
- Q: What should I do if I have questions about the content? A: If you have questions about the content, consider joining online forums or communities focused on data science. You can also reach out to the author via social media.