By Matthew A. Russell
Fb, Twitter, and LinkedIn generate an important quantity 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 positioned? This concise and useful e-book exhibits you the way to respond to those questions and extra. You'll how to mix social net facts, research innovations, and visualization that will help you locate what you've been searching for within the social haystack, in addition to worthy details you didn't comprehend existed.
every one standalone bankruptcy introduces thoughts for mining info in numerous parts of the social net, together with blogs and e-mail. All you must start is a programming heritage and a willingness to profit easy Python instruments.
* Get an easy synopsis of the social net panorama
* Use adaptable scripts on GitHub to reap information from social community APIs akin to Twitter, fb, and LinkedIn
* the way to hire easy-to-use Python instruments to slice and cube the knowledge you gather
* discover social connections in microformats with the XHTML associates community
* practice complicated mining recommendations akin to TF-IDF, cosine similarity, collocation research, record summarization, and clique detection
"Data from the social net is diverse: networks and textual content, now not tables and numbers, are the guideline, and wide-spread question languages are changed with speedily evolving internet carrier APIs. allow Matthew Russell function your consultant to operating with social facts units previous (email, blogs) and new (Twitter, LinkedIn, Facebook). Mining the Social net is a usual successor to Programming Collective Intelligence: a realistic, hands-on method of hacking on info from the social internet with Python." --Jeff Hammerbacher
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Extra info for Mining the Social Web: Finding Needles in the Social Haystack
Ummm…(via @SocialWebMining)”. Extracting relationships from the tweets Because the social web is first and foremost about the linkages between people in the real world, one highly convenient format for storing social web data is a graph. Let’s use NetworkX to build out a graph connecting Twitterers who have retweeted information. We’ll include directionality in the graph to indicate the direction that information is flowing, so it’s more precisely called a digraph. Although the Twitter APIs do offer some capabilities for determining and analyzing statuses that have been retweeted, these APIs are not a great fit for our current use case because we’d have to make a lot of API calls back and forth to the server, which would be a waste of the API calls included in our quota.
A final consideration in analysis is the overall quality of the results. From the standpoint of quality analysis, basic visual inspection of the graph output reveals that there actually is a graph that connects people. Mission accomplished? Yes, but there’s always room for improvement. One consideration is that slight variations in URLs result in multiple nodes potentially appearing in the graph for the same person. com/~matthew in another URL, those two nodes will remain distinct in the graph even though they most likely point to the same resource on the Web.
Via @SocialWebMining)"] >>> for t in example_tweets: ... findall(t) ... [('RT', ' @SocialWebMining')] [('via', ' @SocialWebMining')] In case it’s not obvious, the call to findall returns a list of tuples in which each tuple contains either the matching text or an empty string for each group in the pattern; note that the regex does leave a leading space on the extracted entities, but that’s easily fixed with a call to strip(), as demonstrated in Example 1-11. Since neither of the example tweets contains both of the groups enclosed in the parenthetical expressions, one string is empty in each of the tuples.