大数据洞察：O'Reilly Mining the Social Web 2nd Edition精髓解读

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"OReilly Mining the Social Web 2nd Edition Oct-448页" 《O'Reilly Mining the Social Web》第二版是一本专注于社交媒体数据分析的权威书籍，共计448页，由O'Reilly Media, Inc.出版。这本书的核心在于教导读者如何将社交媒体中的数据转化为有价值的决策依据，适合从初创公司到财富500强的各类企业学习。书中强调了四个重要的趋势： 1. 新的数据收集、管理和分析方法：随着社交媒体的兴起，新的技术和工具使得我们能够以前所未有的方式获取和处理数据。这包括从各种社交平台抓取信息，如Twitter、Facebook和LinkedIn等，并利用先进的分析工具来理解这些数据背后的意义。 2. 云计算：云计算提供了经济高效的存储解决方案和灵活的按需计算能力，特别适合处理大规模数据集。通过云服务，企业可以处理比自身基础设施更大的数据量，且无需大量投资硬件。 3. 数据可视化技术：这些技术将复杂的数据转化为直观的图像，使数据故事更具吸引力。数据可视化对于理解和传达数据分析结果至关重要，它帮助非技术人员理解复杂的信息，提升决策效率。 4. 普及化的数据分析工具：随着Python、R语言和其他开源工具的发展，数据分析能力已经不再局限于专业数据科学家。任何人都可以通过学习这些工具，掌握数据挖掘和分析的基本技能。《Mining the Social Web》第二版作者Matthew A. Russell深入探讨了如何利用上述趋势，掌握大数据的控制权，并将其转化为洞察力。本书不仅提供了理论知识，还可能包含实际案例和代码示例，帮助读者在Strata（O'Reilly主办的数据大会）等相关活动中找到灵感，创建新产品或改进现有产品，理解客户行为，并利用数据获取竞争优势。通过阅读本书，读者将学到如何从社交媒体中提取有价值的信息，进行情感分析、网络分析、用户行为预测等多种分析任务，以驱动业务增长和创新。此外，书中的内容也涵盖了隐私保护、数据伦理和法规遵从性等重要话题，确保读者在进行社交媒体数据挖掘时能够兼顾合法性和道德性。访问O'reilly.com/datatolearnmore，获取更多关于如何利用数据创造价值的信息和资源。

to set up a development environment. If you’ve done some programming before, you

should find that it’s relatively painless to get up and running with the code examples.

Even if you’ve never programmed before but consider yourself the least bit tech-savvy,

I daresay that you could use this book as a starting point to a remarkable journey that

will stretch your mind in ways that you probably haven’t even imagined yet.

To fully enjoy this book and all that it has to offer, you need to be interested in the vast

possibilities for mining the rich data tucked away in popular social websites such as

Twitter, Facebook, LinkedIn, and Google+, and you need to be motivated enough to

download a virtual machine and follow along with the book’s example code in IPython

Notebook, a fantastic web-based tool that features all of the examples for every chapter.

Executing the examples is usually as easy as pressing a few keys, since all of the code is

presented to you in a friendly user interface. This book will teach you a few things that

you’ll be thankful to learn and will add a few indispensable tools to your toolbox, but

perhaps even more importantly, it will tell you a story and entertain you along the way.

It’s a story about data science involving social websites, the data that’s tucked away inside

of them, and some of the intriguing possibilities of what you (or anyone else) could do

with this data.

If you were to read this book from cover to cover, you’d notice that this story unfolds

on a chapter-by-chapter basis. While each chapter roughly follows a predictable tem‐

plate that introduces a social website, teaches you how to use its API to fetch data, and

introduces some techniques for data analysis, the broader story the book tells crescendos

in complexity. Earlier chapters in the book take a little more time to introduce funda‐

mental concepts, while later chapters systematically build upon the foundation from

earlier chapters and gradually introduce a broad array of tools and techniques for mining

the social web that you can take with you into other aspects of your life as a data scientist,

analyst, visionary thinker, or curious reader.

Some of the most popular social websites have transitioned from fad to mainstream to

household names over recent years, changing the way we live our lives on and off the

Web and enabling technology to bring out the best (and sometimes the worst) in us.

Generally speaking, each chapter of this book interlaces slivers of the social web along

with data mining, analysis, and visualization techniques to explore data and answer the

following representative questions:

•

Who knows whom, and which people are common to their social networks?

•

How frequently are particular people communicating with one another?

•

Which social network connections generate the most value for a particular niche?

•

How does geography affect your social connections in an online world?

xiv | Preface

• Who are the most influential/popular people in a social network?

• What are people chatting about (and is it valuable)?

• What are people interested in based upon the human language that they use in a

digital world?

The answers to these basic kinds of questions often yield valuable insight and present

lucrative opportunities for entrepreneurs, social scientists, and other curious practi‐

tioners who are trying to understand a problem space and find solutions. Activities such

as building a turnkey killer app from scratch to answer these questions, venturing far

beyond the typical usage of visualization libraries, and constructing just about anything

state-of-the-art are not within the scope of this book. You’ll be really disappointed if

you purchase this book because you want to do one of those things. However, this book

does provide the fundamental building blocks to answer these questions and provide a

springboard that might be exactly what you need to build that killer app or conduct that

research study. Skim a few chapters and see for yourself. This book covers a lot of ground.

Python-Centric Technology

This book intentionally takes advantage of the Python programming language for all of

its example code. Python’s intuitive syntax, amazing ecosystem of packages that trivialize

API access and data manipulation, and core data structures that are practically JSON

make it an excellent teaching tool that’s powerful yet also very easy to get up and running.

As if that weren’t enough to make Python both a great pedagogical choice and a very

pragmatic choice for mining the social web, there’s IPython Notebook, a powerful, in‐

teractive Python interpreter that provides a notebook-like user experience from within

your web browser and combines code execution, code output, text, mathematical type‐

setting, plots, and more. It’s difficult to imagine a better user experience for a learning

environment, because it trivializes the problem of delivering sample code that you as

the reader can follow along with and execute with no hassles. Figure P-1 provides an

illustration of the IPython Notebook experience, demonstrating the dashboard of note‐

books for each chapter of the book. Figure P-2 shows a view of one notebook.

Preface | xv

Every chapter in this book has a corresponding IPython Notebook with example code

that makes it a pleasure to study the code, tinker around with it, and customize it for

your own purposes. If you’ve done some programming but have never seen Python

syntax, skimming ahead a few pages should hopefully be all the confirmation that you

need. Excellent documentation is available online, and the official Python tutorial is a

good place to start if you’re looking for a solid introduction to Python as a programming

language. This book’s Python source code is written in Python 2.7, the latest release of

the 2.x line. (Although perhaps not entirely trivial, it’s not too difficult to imagine using

some of the automated tools to up-convert it to Python 3 for anyone who is interested

in helping to make that happen.)

IPython Notebook is great, but if you’re new to the Python programming world, advising

you to just follow the instructions online to configure your development environment

would be a bit counterproductive (and possibly even rude). To make your experience

with this book as enjoyable as possible, a turnkey virtual machine is available that has

IPython Notebook and all of the other dependencies that you’ll need to follow along

with the examples from this book preinstalled and ready to go. All that you have to do

is follow a few simple steps, and in about 15 minutes, you’ll be off to the races. If you

have a programming background, you’ll be able to configure your own development

environment, but my hope is that I’ll convince you that the virtual machine experience

is a better starting point.

See Appendix A for more detailed information on the virtual ma‐

chine experience for this book. Appendix C is also worth your atten‐

tion: it presents some IPython Notebook tips and common Python

programming idioms that are used throughout this book’s source code.

Whether you’re a Python novice or a guru, the book’s latest bug-fixed source code and

accompanying scripts for building the virtual machine are available on GitHub, a social

Git repository that will always reflect the most up-to-date example code available. The

hope is that social coding will enhance collaboration between like-minded folks who

want to work together to extend the examples and hack away at fascinating problems.

Hopefully, you’ll fork, extend, and improve the source—and maybe even make some

new friends or acquaintances along the way.

The official GitHub repository containing the latest and greatest bug-

fixed source code for this book is available at http://bit.ly/MiningThe

SocialWeb2E.

Preface | xvii

Improvements Specific to the Second Edition

When I began working on this second edition of Mining the Social Web, I don’t think I

quite realized what I was getting myself into. What started out as a “substantial update”

is now what I’d consider almost a rewrite of the first edition. I’ve extensively updated

each chapter, I’ve strategically added new content, and I really do believe that this second

edition is superior to the first in almost every way. My earnest hope is that it’s going to

be able to reach a much wider audience than the first edition and invigorate a broad

community of interest with tools, techniques, and practical advice to implement ideas

that depend on munging and analyzing data from social websites. If I am successful in

this endeavor, we’ll see a broader awareness of what it is possible to do with data from

social websites and more budding entrepreneurs and enthusiastic hobbyists putting

social web data to work.

A book is a product, and first editions of any product can be vastly improved upon,

aren’t always what customers ideally would have wanted, and can have great potential

if appropriate feedback is humbly accepted and adjustments are made. This book is no

exception, and the feedback and learning experience from interacting with readers and

consumers of this book’s sample code over the past few years have been incredibly

important in shaping this book to be far beyond anything I could have designed if left

to my own devices. I’ve incorporated as much of that feedback as possible, and it mostly

boils down to the theme of simplifying the learning experience for readers.

Simplification presents itself in this second edition in a variety of ways. Perhaps most

notably, one of the biggest differences between this book and the previous edition is

that the technology toolchain is vastly simplified, and I’ve employed configuration

management by way of an amazing virtualization technology called Vagrant. The pre‐

vious edition involved a variety of databases for storage, various visualization toolkits,

and assumed that readers could just figure out most of the installation and configuration

by reading the online instructions.

This edition, on the other hand, goes to great lengths to introduce as few disparate

technology dependencies as possible and presents them all with a virtual machine ex‐

perience that abstracts away the complexities of software installation and configuration,

which are sometimes considerably more challenging than they might initially seem.

From a certain vantage point, the core toolbox is just IPython Notebook and some third-

party package dependencies (all of which are versioned so that updates to open source

software don’t cause code breakage) that come preinstalled on a virtual machine. Inline

visualizations are even baked into the IPython Notebooks, rendering from within IPy‐

thon Notebook itself, and are consolidated down to a single JavaScript toolkit (D3.js)

that maintains visually consistent aesthetics across the chapters.

xviii | Preface

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大数据洞察：O'Reilly Mining the Social Web 2nd Edition精髓解读

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