Matthew Russell has completed nearly 50 publications on technology, including work that has appeared at scientific conferences and in Linux Journal and Make magazine. He is also the author of Dojo: The Definitive Guide (O’Reilly). Matthew is Vice President of Engineering at Digital Reasoning Systems and is Founder & Principal at Zaffra, a firm focused on agile web development.
Popular social networks such as Facebook, Twitter, and LinkedIn generate a tremendous amount of valuable social data. Who's talking to whom? What are they talking about? How often are they talking? Where are they located? This concise and practical book shows you how to answer these types of questions and more. Each chapter presents a soup-to-nuts approach that combines popular social web data, analysis techniques, and visualization to help you find the needles in the social haystack you've been looking for -- and some you didn't know were there.
With Mining the Social Web, intermediate-to-advanced Python programmers will learn how to collect and analyze social data in way that lends itself to hacking as well as more industrial-strength analysis. The book is highly readable from cover to cover and tells a coherent story, but you can go straight to chapters of interest if you want to focus on a specific topic.
Get a concise and straightforward synopsis of the social web landscape so you know which 20% of the space to spend 80% of your time on
Use easily adaptable scripts hosted on GitHub to harvest data from popular social network APIs including Twitter, Facebook, and LinkedIn
Learn how to slice and dice social web data with easy-to-use Python tools, and apply more advanced mining techniques such as TF-IDF, cosine similarity, collocation analysis, document summarization, and clique detection
Build interactive visualizations with easily adaptable web technologies built upon HTML5 and JavaScript toolkits
This book is still in progress, but you can get going on this technology through our Rough Cuts edition, which lets you read the manuscript as it's being written, either online or via PDF.
via http://oreilly.com/catalog/9781449394844/
Amazon: http://www.amazon.com/Mining-Social-Web-Finding-Haystack/dp/1449388345/
yes, damn beaver -,-# 社交网站的DM需要用直推来隐藏看似复杂却又简单,做起来简单却确实不是随便谁都能做好的工作。 UPLOAD YOUR SOUL TO THE ULTIMATE INTERNET!哈哈哈哈!
评分虽然使用的语言是python,而且分析的网站都是国内被禁的网站,但是读完这本书后,感到很受启发,其实如果你懂了这本书中的内容,分析其他社交网站也会得心应手,比如说像国内的sina微博,人家提供的API也很有价值啊,你读完这本书,收获会很大。
评分原本是想学些数据分析的算法和思想,但是拿到这本书之后挺失望。看到第四章,全在讲如何使用twitter等社交网站的api。 只能当拓展知识面看看,了解下书里面讲到的开源工具。 另外,书的价格还不算便宜。
评分Facebook、Twitter和LinkedIn产生了大量宝贵的社交数据,但是你怎样才能找出谁通过社交媒介正在进行联系?他们在讨论些什么?或者他们在哪儿?这本简洁而且具有可操作性的书将揭示如何回答这些问题甚至更多的问题。你将学到如何组合社交网络数据、分析技术,如何通过可视化帮助你...
评分虽然使用的语言是python,而且分析的网站都是国内被禁的网站,但是读完这本书后,感到很受启发,其实如果你懂了这本书中的内容,分析其他社交网站也会得心应手,比如说像国内的sina微博,人家提供的API也很有价值啊,你读完这本书,收获会很大。
社交网络里面的方方面面都有涉及,问题讲的比较透彻,通过算法背后的一些数据,帮助理解follow和friend单向/双向关系直接的细微差别和适用场景,还有更多诸如此类的灵感
评分不容易,终于读完了。
评分Python相关库使用说明书,要是那些库你都没听过,那还是稍微有点用
评分很实用,寓教于乐
评分一般吧
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