Mining the Social Web: Finding Needles in the Social Haystack

By Matthew A. Russell

Fb, Twitter, and LinkedIn generate a huge volume of precious social info, yet how are you going to discover who's making connections with social media, what they’re speaking approximately, or the place they’re situated? This concise and sensible ebook exhibits you ways to respond to those questions and extra. You'll methods to mix social internet facts, research thoughts, and visualization that can assist you locate what you've been searching for within the social haystack, in addition to worthy info you didn't be aware of existed.

every one standalone bankruptcy introduces innovations for mining info in numerous components of the social internet, together with blogs and e mail. All you want to start is a programming heritage and a willingness to profit easy Python instruments.

* Get an easy synopsis of the social internet panorama
* Use adaptable scripts on GitHub to reap facts from social community APIs similar to Twitter, fb, and LinkedIn
* methods to hire easy-to-use Python instruments to slice and cube the knowledge you acquire
* discover social connections in microformats with the XHTML acquaintances community
* follow complicated mining ideas resembling TF-IDF, cosine similarity, collocation research, rfile summarization, and clique detection
* construct interactive visualizations with net applied sciences dependent upon HTML5 and JavaScript toolkits

"Data from the social net is diversified: networks and textual content, now not tables and numbers, are the rule of thumb, and usual question languages are changed with swiftly evolving net provider APIs. allow Matthew Russell function your consultant to operating with social facts units outdated (email, blogs) and new (Twitter, LinkedIn, Facebook). Mining the Social internet is a normal successor to Programming Collective Intelligence: a realistic, hands-on method of hacking on information from the social internet with Python." --Jeff Hammerbacher

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U':d', u'2', u'will'] >>> freq_dist. keys()[-50:] # 50 least common tokens [u'what?! ', u'whens', u'where', u'while', u'white', u'whoever', u'whoooo!!!! ', u'whose', u'wiating', u'wii', u'wiig', u'win... ', u'wink. ', u'wknd. ', u'wohh', u'won', u'wonder', u'wondering', u'wootwoot! ', u'worked', u'worth', u'xo. ', u'xx', u'ya', u'ya<3miranda', u'yay', u'yay! ', u'ya\u2665', u'yea', u'yea. ', u'yeaa', u'yeah! ', u'yeah. ', u'yeahhh. ', u'yes,', u'yes;)', u'yess', u'yess,', u'you!!!!! ', u"you'll", u'you+snl=', u'you,', u'youll', u'youtube?? ', u'youu<3', u'youuuuu', u'yum', u'yumyum', u'~', u'\xac\xac'] observe Python 2. 7 extra a collections. Counter (http://docs. python. org/library/collections. html#collections. Counter) type that allows counting operations. chances are you'll locate it invaluable if you’re in a scenario the place you can’t simply set up NLTK, or in the event you simply are looking to scan with the most recent and maximum periods from Python’s typical library. a really speedy skim of the consequences from instance 1-9 exhibits lot extra beneficial details is carried within the widespread tokens than the rare tokens. even though a few paintings would have to be performed to get a computer to acknowledge as a lot, the widespread tokens confer with entities equivalent to humans, instances, and actions, whereas the rare phrases volume to more often than not noise from which no significant end will be drawn. first thing you've gotten spotted concerning the such a lot widespread tokens is that “snl” is on the best of the record. provided that it's the foundation of the unique seek question, this isn’t extraordinary in any respect. the place it will get extra fascinating is in the event you skim the rest tokens: there's it seems that loads of chatter a couple of fellow named Justin Bieber, as evidenced by way of the tokens @justinbieber, justin, and bieber. an individual acquainted with SNL could additionally comprehend that the occurrences of the tokens “tina” and “fey” aren't any twist of fate, given Tina Fey’s longstanding association with the exhibit. expectantly, it’s now not too tricky (as a human) to skim the tokens and shape the conjecture that Justin Bieber is a well-liked man, and lot of parents have been very excited that he was once going to be at the exhibit at the Saturday night the hunt question used to be performed. At this element, you are considering, “So what? i'll skim a number of tweets and deduce as a lot. ” whereas which may be actual, could you need to do it 24/7, or pay somebody to do it for you round the clock? And what if you happen to have been operating in a unique area that wasn’t as amenable to skimming random samples of brief message blurbs? the purpose is that frequency research is an easy, but very strong software that shouldn’t be ignored simply because it’s so seen. to the contrary, it's going to be attempted out first for exactly the cause that it’s so seen and easy. therefore, one initial takeaway here's that the applying of an easy method can get you particularly far towards answering the query, “What are humans speaking approximately at once? ” As a last statement, the presence of “rt” is additionally a crucial clue as to the character of the conversations occurring.

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