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Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More

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Facebook, Twitter, and LinkedIn generate a tremendous amount of valuable social data, but how can you find out who's making connections with social media, what they’re talking about, or where they’re located? This concise and practical book shows you how to answer these questions and more. You'll learn how to combine social web data, analysis techniques, and visualization Facebook, Twitter, and LinkedIn generate a tremendous amount of valuable social data, but how can you find out who's making connections with social media, what they’re talking about, or where they’re located? This concise and practical book shows you how to answer these questions and more. You'll learn how to combine social web data, analysis techniques, and visualization to help you find what you've been looking for in the social haystack, as well as useful information you didn't know existed. Each standalone chapter introduces techniques for mining data in different areas of the social Web, including blogs and email. All you need to get started is a programming background and a willingness to learn basic Python tools. Get a straightforward synopsis of the social web landscape Use adaptable scripts on GitHub to harvest data from social network APIs such as Twitter, Facebook, and LinkedIn Learn how to employ easy-to-use Python tools to slice and dice the data you collect Explore social connections in microformats with the XHTML Friends Network Apply advanced mining techniques such as TF-IDF, cosine similarity, collocation analysis, document summarization, and clique detection Build interactive visualizations with web technologies based upon HTML5 and JavaScript toolkits "Data from the social Web is different: networks and text, not tables and numbers, are the rule, and familiar query languages are replaced with rapidly evolving web service APIs. Let Matthew Russell serve as your guide to working with social data sets old (email, blogs) and new (Twitter, LinkedIn, Facebook). Mining the Social Web is a natural successor to Programming Collective Intelligence: a practical, hands-on approach to hacking on data from the social Web with Python." —Jeff Hammerbacher


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Facebook, Twitter, and LinkedIn generate a tremendous amount of valuable social data, but how can you find out who's making connections with social media, what they’re talking about, or where they’re located? This concise and practical book shows you how to answer these questions and more. You'll learn how to combine social web data, analysis techniques, and visualization Facebook, Twitter, and LinkedIn generate a tremendous amount of valuable social data, but how can you find out who's making connections with social media, what they’re talking about, or where they’re located? This concise and practical book shows you how to answer these questions and more. You'll learn how to combine social web data, analysis techniques, and visualization to help you find what you've been looking for in the social haystack, as well as useful information you didn't know existed. Each standalone chapter introduces techniques for mining data in different areas of the social Web, including blogs and email. All you need to get started is a programming background and a willingness to learn basic Python tools. Get a straightforward synopsis of the social web landscape Use adaptable scripts on GitHub to harvest data from social network APIs such as Twitter, Facebook, and LinkedIn Learn how to employ easy-to-use Python tools to slice and dice the data you collect Explore social connections in microformats with the XHTML Friends Network Apply advanced mining techniques such as TF-IDF, cosine similarity, collocation analysis, document summarization, and clique detection Build interactive visualizations with web technologies based upon HTML5 and JavaScript toolkits "Data from the social Web is different: networks and text, not tables and numbers, are the rule, and familiar query languages are replaced with rapidly evolving web service APIs. Let Matthew Russell serve as your guide to working with social data sets old (email, blogs) and new (Twitter, LinkedIn, Facebook). Mining the Social Web is a natural successor to Programming Collective Intelligence: a practical, hands-on approach to hacking on data from the social Web with Python." —Jeff Hammerbacher

30 review for Mining the Social Web: Data Mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More

  1. 4 out of 5

    Doug Lautzenheiser

    This short book might have more appropriately been titled, "How I Personally Mined the Social Web using Python." Without giving too much explanation, the author provides samples of his Python routines. Where another author might spend an entire chapter (if not the whole book) explaining a technological topic, Russell just makes a comment and moves on to his code examples. If you are comfortable with, "Install this, run that command, and now copy my code..." then this is an okay book. This is bas This short book might have more appropriately been titled, "How I Personally Mined the Social Web using Python." Without giving too much explanation, the author provides samples of his Python routines. Where another author might spend an entire chapter (if not the whole book) explaining a technological topic, Russell just makes a comment and moves on to his code examples. If you are comfortable with, "Install this, run that command, and now copy my code..." then this is an okay book. This is basically a Python cookbook with Social Media recipes. It covers APIs useful for Google e-mail, Twitter, Facebook, and LinkedIn. As such, it was interesting reading to see how it is done, but this is not a primer on how to do it.

  2. 4 out of 5

    Ietrio

    A book on data mining. Interesting. Than I open it! Wow! So data mining starts with why people are on Twitter. A cute bullet list about the human need to be heard. Okay. Maybe it's just a slip. Turn the page over. Well, an insightful paragraph about Twitter having been started at 140 characters! Amazing! Probably your data mining apps won't work if you set the Tweet size to 512 characters! Next paragraph. More goodies! Twitter is all the rage. Really? Not really. It's interesting for journalists p A book on data mining. Interesting. Than I open it! Wow! So data mining starts with why people are on Twitter. A cute bullet list about the human need to be heard. Okay. Maybe it's just a slip. Turn the page over. Well, an insightful paragraph about Twitter having been started at 140 characters! Amazing! Probably your data mining apps won't work if you set the Tweet size to 512 characters! Next paragraph. More goodies! Twitter is all the rage. Really? Not really. It's interesting for journalists paid by the word as they can put a twist to a badly phrased statement. I turn the page. And, more data mining information. @HomerSimpson is not a real person! And it does not stop here! Can you imagine it is in relation with the Fox sitcom and not the president of North Korea! Amazing! So how can I do such high quality data mining? Well, let's see a page about TweetDeck, which does data mining? No, but by this time you already know this book isn't about data mining. More precious data mining somehow related to this load of manure: the importance of PyDoc. So you have to know Python to follow this clown's examples, but data mining is about PyDoc. Or something. The data is said to be at Github for free, because the editor and the author make too little money to invest in a domain. But somehow that is lost and the links are obfuscated with bit.ly links to track you. So are you learning about data mining or these unscrupulous characters are data mining your account?

  3. 4 out of 5

    Louis

    The hardest part of learning a data analysis method is not in implementing the method, it is applying the method in the context of a real data problem. And data mining and machine learning texts often skirt the issue by using pre-processed data sets and problems defined to fit the method being taught. Russell uses analysis of social media sites to set a context where you start from having to gain access to real data sets, clean and transform the data into forms that your analytical libraries can The hardest part of learning a data analysis method is not in implementing the method, it is applying the method in the context of a real data problem. And data mining and machine learning texts often skirt the issue by using pre-processed data sets and problems defined to fit the method being taught. Russell uses analysis of social media sites to set a context where you start from having to gain access to real data sets, clean and transform the data into forms that your analytical libraries can make sense of, and then use the results to make a conclusion. For that, it rates a place along any other text that focuses more on the analytical methodology itself. What I most appreciated about this book was the work put into converting data from one format to another. From the beginning, when he works with data pulled using a services API, then getting that into a format that another library requires, then getting those results into a data mining framework for analysis. Following his flow has helped me understand the methods better. And these examples of processing data from format to format is something that gets my students stuck before they get really started in a project. I especially appreciated the chapters that worked with the Natural Language Toolkit (NLTK) and the NetworkX graph libraries. These examples helped me get pass what was the hard part for me in working with these libraries in previous encounters. The virtual machine is also very helpful. I have always found the hardest part of working with Python for analytic computing has been teaching my collaborators how to get set up. And in data mining this is even harder than standard. I was able to get through his book installing everything on one machine, but on another I used the author's virtual machine, and I have pointed a student who was working with me to the virtual machine as well. This is a great book to work through the mess of implementing data mining methods in real situations. It is not a theory book, but it serves its purpose well. Note: I received a free electronic copy of this book from the O'Reilly Press Blogger program.

  4. 4 out of 5

    Minh Nhật

    đọc nhiều cuốn về chủ đề data science/machine learning thì cuốn này rất rất ổn. 4.5 nhưng đang vui quá tay tí :)) p/s: định mining goodreads thử mà thấy respoone toàn xml nản quá T^T trên này có cả group dev nó bảo api gì toàn tầm chục năm :v. Nghiêm túc thì bạn nào có muốn thử không nhỉ ???

  5. 4 out of 5

    Claire

    What I found most useful from this book was the information these data scientists held within these pages about GitHub. I didn't know anything about this before I opened this book up. The kawaii icons noting each point to understand in particular are absolutely adorable, as well! I think for a textbook about the new world we're living within today it comes across incredibly nicely. BUT HONESTLY WHAT MADE ME LAUGH THE HARDEST WAS THE README.1st AT THE BEGINNING - since, I thought to myself, didn't What I found most useful from this book was the information these data scientists held within these pages about GitHub. I didn't know anything about this before I opened this book up. The kawaii icons noting each point to understand in particular are absolutely adorable, as well! I think for a textbook about the new world we're living within today it comes across incredibly nicely. BUT HONESTLY WHAT MADE ME LAUGH THE HARDEST WAS THE README.1st AT THE BEGINNING - since, I thought to myself, didn't we all click those open in our little games? At least I definitely did! I remember so clearly~! When I was a little girl like six or seven I would sit with my bird chirping up a storm right on my shoulder as I read the README document the whole way through before I played the cute little text game. And died a miserable text death, of course. But the nice thing was that you could start right over again with exactly the same stats! People asked me "What exactly do they mean by 'mining' in that regard?" and I tell them that you don't typically need your lighted helmet when you're doing this kind of social mining, but it seems to me that it is looking at general trends and making projections for the future. And, also, look at the adorable woodland creature on the cover!

  6. 4 out of 5

    Clem

    This review has been hidden because it contains spoilers. To view it, click here. About to read hope it will be nice

  7. 4 out of 5

    Richard

    Very Helpful I learned a lot and gathered valuable information. I suggest this book anyone lookin for information on social media and be well informed

  8. 4 out of 5

    Brad Rice

    I was given a free e-book and asked by O'Reilly to review it in exchange. I was excited for the opportunity since I think that having the ability to mine the social web is important. I was also happy that the author utilized Python as the programming language of choice to show how this is to be done. I have been using Python as a tool now for about a year and have found it to be my preferred server side scripting language for web app development. If you are a php, perl, ruby or java developer, I I was given a free e-book and asked by O'Reilly to review it in exchange. I was excited for the opportunity since I think that having the ability to mine the social web is important. I was also happy that the author utilized Python as the programming language of choice to show how this is to be done. I have been using Python as a tool now for about a year and have found it to be my preferred server side scripting language for web app development. If you are a php, perl, ruby or java developer, I think you could pretty easily transfer the techniques shown across to your choice platform. The book is not focused on Python development per-se, but I think a certain amount of knowledge of that language is helpful to understanding the book. One interesting benefit is the appendix, where the author walks you through the use of IPython Notebooks as a way to show example code and execute it. A virtual machine is setup and then run on a port for you to execute live code in a browser. After getting setup, getting to the heart of the matter, the author does a good job of covering the main aspects of mining the more notable social media sites such as Facebook, Twitter and LinkedIn. Introductions to all the api's and example code showing how to access the data on those sites are well written and explained. The book is an excellent cookbook and a must have for the technically minded. However, a shortcoming may be that it does not cover much in the way of theory or objectives of data mining and analysis. While this is an excellent book of how-to, the why of social media mining is left to other sources.

  9. 5 out of 5

    Ehnaton

    Я завжди не розумів формату cookbook. Який сенс давати розрізнені куски коду, які виконують те, що треба, але не пояснюють основу. Тут я обламався, тому що від соціалок більше нічого і не треба. Суть - зібрати набір даних соціальних контактів, і знайти патерни, які найчастіше трапляються, і потім красиво їх звізуалізувати. Автор вдається перейти певну межу, і таки навчити зацікавленого прогера копатись в цьому сирому і зашумленому матеріалі, і робити це ефективно. Особливо сподобалась ідея практ Я завжди не розумів формату cookbook. Який сенс давати розрізнені куски коду, які виконують те, що треба, але не пояснюють основу. Тут я обламався, тому що від соціалок більше нічого і не треба. Суть - зібрати набір даних соціальних контактів, і знайти патерни, які найчастіше трапляються, і потім красиво їх звізуалізувати. Автор вдається перейти певну межу, і таки навчити зацікавленого прогера копатись в цьому сирому і зашумленому матеріалі, і робити це ефективно. Особливо сподобалась ідея практичного використовувати відстані Жаккара для визначення схожості користувацької аудиторії кількох конкурентів. Клепав весь день код, потім зробив аналіз результатів, і презентував керівництву. Виявилось, що наш продукт буде корисно несподівано розвивати трохи в іншу сторону. От це правильний cookbook. Рекомендую.

  10. 5 out of 5

    Arnob

    Just started with the book but it looks like an interesting read so far Just started with the book but it looks like an interesting read so far The topics for the book cover a wide range of data mining practices and the book seems like a great way to get into Python and Data Science.

  11. 4 out of 5

    Wael Al-alwani

    Excellent book.. its beauty lies in the loads of ideas it gives, efficient ways to implement them, and the tools it talks about. What this book lacks IMHO are the extra detailed discussions on why x approach was followed and what's the rationale behind that.. as one commenter said, the book has too many How's but few Why's.

  12. 4 out of 5

    Nicolas Morin

    I don't usually enter tech books here, because I rarely read them from cover to cover. Bit this one I did. It's well written, comprehensive. My only caveat is the author's fondness with the heavy VM he uses for his examples...

  13. 5 out of 5

    Osamuyi Okpame

    Too technical ...

  14. 5 out of 5

    Sefa

    Good book to learn extracting data using Facebook/Twitter/LinkedIn etc. APIs. Doesn't focus enough on how to extract meaning from the data, though.

  15. 5 out of 5

    Niyikiza Aimable

    A great start for anyone interested in data mining. Basic python hacking skills required.

  16. 4 out of 5

    Gary Lang

    A lot of interesting stuff to play with here. TBD: convert some of this to C#

  17. 4 out of 5

    Elena

    Ejemplos útiles en python. Bastante bien.

  18. 4 out of 5

    Alberto

  19. 4 out of 5

    Wes Stahler

  20. 5 out of 5

    Jurica

  21. 4 out of 5

    Nicholas Sardo

  22. 4 out of 5

    Raymond W. Bachert

  23. 4 out of 5

    Yaser

  24. 4 out of 5

    Loan Huynh

  25. 4 out of 5

    Felipe Guerrero

  26. 5 out of 5

    Dan Dittenhafer

  27. 4 out of 5

    Pedro Araújo

  28. 5 out of 5

    Nicolas Richard

  29. 5 out of 5

    Dongdong Lee

  30. 5 out of 5

    Jens

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