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Data Science for Web3

You're reading from   Data Science for Web3 A comprehensive guide to decoding blockchain data with data analysis basics and machine learning cases

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Product type Paperback
Published in Dec 2023
Publisher Packt
ISBN-13 9781837637546
Length 344 pages
Edition 1st Edition
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Author (1):
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Gabriela Castillo Areco Gabriela Castillo Areco
Author Profile Icon Gabriela Castillo Areco
Gabriela Castillo Areco
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Toc

Table of Contents (23) Chapters Close

Preface 1. Part 1 Web3 Data Analysis Basics
2. Chapter 1: Where Data and Web3 Meet FREE CHAPTER 3. Chapter 2: Working with On-Chain Data 4. Chapter 3: Working with Off-Chain Data 5. Chapter 4: Exploring the Digital Uniqueness of NFTs – Games, Art, and Identity 6. Chapter 5: Exploring Analytics on DeFi 7. Part 2 Web3 Machine Learning Cases
8. Chapter 6: Preparing and Exploring Our Data 9. Chapter 7: A Primer on Machine Learning and Deep Learning 10. Chapter 8: Sentiment Analysis – NLP and Crypto News 11. Chapter 9: Generative Art for NFTs 12. Chapter 10: A Primer on Security and Fraud Detection 13. Chapter 11: Price Prediction with Time Series 14. Chapter 12: Marketing Discovery with Graphs 15. Part 3 Appendix
16. Chapter 13: Building Experience with Crypto Data – BUIDL 17. Chapter 14: Interviews with Web3 Data Leaders 18. Index 19. Other Books You May Enjoy Appendix 1
1. Appendix 2
2. Appendix 3

Adding social networks to our dataset

Web3 is an online industry so everything that happens online, from opinions to interactions, holds significant influence.

Sentiment analysis, gauging reactions to products or tokens, plays a crucial role for marketing teams, analysts, and traders alike. A noteworthy example illustrating the importance of such metrics is the CoinStats Fear and Greed indicator. This index, available at https://coinstats.app/fear-and-greed/, incorporates social media posts, among other factors, to measure market sentiment.

Figure 3.20 – Crypto Fear and Greed Indicator

Figure 3.20 – Crypto Fear and Greed Indicator

According to CoinStats’ explanation, the index combines data from various sources. To capture psychological momentum, they also draw insights from social media interactions on X, focusing on specific hashtags that carry both fear and greed components, which contribute to the overall calculation. The social media component holds a 15% weight in the final...

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